# Beneath the Tip of the Iceberg: Current Challenges and New Directions in
Sentiment Analysis Research

Soujanya Poriaα∗, Devamanyu Hazarikaβ, Navonil Majumderα, Rada Mihalceaγ
αInformation Systems Technology and Design, Singapore University of Technology
and Design, Singapore
β School of Computing, National University of Singapore, Singapore
γ Electrical Engineering and Computer Science, University of Michigan,
Michigan, USA
We dedicate this paper to the memory of Prof. Janyce Wiebe,
who had always believed in the future of the field of sentiment analysis. ∗
Corresponding author (e-mail: sporia@sutd.edu.sg)Soujanya Poria can be
contacted at<EMAIL_ADDRESS>Devamanyu Hazarika can be contacted at
<EMAIL_ADDRESS>Navonil Majumder can be contacted at
<EMAIL_ADDRESS>Rada Mihalcea can be contacted at
<EMAIL_ADDRESS>
###### Abstract

Sentiment analysis as a field has come a long way since it was first
introduced as a task nearly 20 years ago. It has widespread commercial
applications in various domains like marketing, risk management, market
research, and politics, to name a few. Given its saturation in specific
subtasks — such as sentiment polarity classification — and datasets, there is
an underlying perception that this field has reached its maturity. In this
article, we discuss this perception by pointing out the shortcomings and
under-explored, yet key aspects of this field necessary to attain _true_
sentiment understanding. We analyze the significant leaps responsible for its
current relevance. Further, we attempt to chart a possible course for this
field that covers many overlooked and unanswered questions.

###### Index Terms:

Natural Language Processing, Sentiment Analysis, Emotion Recognition, Aspect
Based Sentiment Analysis, Sarcasm Analysis, Sentiment-aware Dialogue
Generation, Bias in Sentiment Analysis Systems.

##  1 Introduction

Sentiment analysis, also known as opinion mining, is a research field that
aims at understanding the underlying sentiment of unstructured content. E.g.,
in this sentence, “John dislikes the camera of iPhone 7”, according to the
technical definition (Liu, 2012) of sentiment analysis, John plays the role of
the _opinion holder_ exposing his _negative sentiment_ towards the _aspect_ –
camera of the _entity_ – iPhone 7. Since its early beginnings (Pang et al.,
2002; Turney, 2002), sentiment analysis has established itself as an
influential field of research with widespread applications in industry. Its
ever-increasing popularity and demand stem from the individuals, businesses,
and governments interested in understanding people’s views about products,
political agendas, or marketing campaigns. Public opinion also stimulates
market trends, which makes it relevant for financial predictions. Furthermore,
education and healthcare sectors make use of sentiment analysis for behavioral
analysis of students and patients.

Over the years, the scope for innovation and commercial demand has jointly
driven research in sentiment analysis. However, there has been an emerging
perception that the problem of sentiment analysis is merely a text/content
categorization task – one that requires content to be classified into two or
three categories of sentiments: positive, negative, or neutral. This has led
to a belief among researchers that sentiment analysis has reached its
saturation. Through this work, we set forth to address this misconception.

Figure 1 shows that many benchmark datasets on the polarity detection subtask
of sentiment analysis, like IMDB or SST-2, have reached saturation points, as
reflected by the near-perfect scores achieved by many modern data-driven
methods. However, this does not imply that sentiment analysis is solved.
Rather, we believe that this perception of saturation has manifested from
excessive research publications that focus only on shallow sentiment
understanding, such as k-way text classification, whilst ignoring other key
un- and under-explored problems relevant to this research field.

Figure 1: Performance trends of recent models on IMDB (Maas et al., 2011),
SST-2, SST-5 (Socher et al., 2013) and Semeval (Pontiki et al., 2014)
datasets. The tasks involve sentiment classification in either aspect or
sentence level. Note: Data obtained from
https://paperswithcode.com/task/sentiment-analysis. The labels on the graphs
are either citations or system names that are also retrieved from
https://paperswithcode.com/task/sentiment-analysis.

Liu (2015) presents sentiment analysis as mini-NLP, given its reliance on
topics covering almost the entirety of NLP. Similarly, Cambria et al. (2017)
characterize sentiment analysis as a big suitcase of subtasks and subproblems,
involving open syntactic, semantic, and pragmatic problems. As such, there
remain several open research directions to be extensively studied, such as
understanding motive and cause of sentiment, sentiment dialogue generation,
sentiment reasoning, and so on. At its core, effective inference of sentiment
requires an understanding of multiple fundamental problems in NLP. These
include assigning polarities to aspects, negation handling, resolving co-
references, and identifying syntactic dependencies to exploit sentiment flow.
The figurative nature of language also influences sentiment analysis, often
exploited using linguistic devices, such as sarcasm and irony. This complex
composition of multiple tasks makes sentiment analysis a challenging yet
interesting research space.

Figure 1 also demonstrates that the methods with a contextual language model
as their backbone, much like in other areas of NLP, have dominated these
benchmark datasets. Equipped with millions or billions of parameters,
transformer-based networks such as BERT (Devlin et al., 2019), RoBERTa (Liu et
al., 2019), and their variants have pushed the state-of-the-art to new
heights. Despite this performance boost, these models are opaque, and their
inner-workings are not fully understood. Thus, the question that remains is
how far have we progressed since the beginning of sentiment analysis? (Pang et
al., 2002)

The importance of lexical, syntactical, and contextual features has been
acknowledged numerous times in the past. Recently, due to the advent of the
powerful contextualized word embeddings and networks like BERT, we can compute
much better representations of such features. Does this entail _true_
sentiment understanding? Not likely, as we are far from any significant
achievement in _multi-faceted sentiment research_ , such as the underlying
motivations behind an expressed sentiment, sentiment reasoning, and so on. As
members of this research community, we believe that we should strive to move
past simple classification as the benchmark of progress and instead direct our
efforts towards learning tangible sentiment understanding. Taking a step in
this direction would include analyzing, customizing, and training modern
architectures in the context of sentiment, emphasizing fine-grained analysis
and exploration of parallel new directions, such as multimodal learning,
sentiment reasoning, sentiment-aware natural language generation, and
figurative language.

The primary goal of this paper is to motivate new researchers approaching this
area. We begin by summarizing the key milestones reached (Figure 3) in the
last two decades of sentiment analysis research, followed by opening the
discussion on new and understudied research areas of sentiment analysis. We
also identify some of the critical shortcomings in several sub-fields of
sentiment analysis and describe potential research directions. This paper is
not intended as a survey of the field – we mainly cover a small number of key
contributions that have either had a seminal impact on this field or have the
potential to open new avenues. Our intention, thus, is to draw attention to
key research topics within the broad field of sentiment analysis and identify
critical directions left to be explored. We also uncover promising new
frameworks and applications that may drive sentiment analysis research in the
near future.

The rest of the paper is organized as follows: Section 2 briefly describes the
key developments and achievements in the sentiment analysis research; we
discuss the future directions of sentiment analysis research in Section 3; and
finally, Section 4 concludes the paper. We illustrate the overall organization
of the paper in Figure 2. We curate all the articles, that cover the past and
future of sentiment analysis (see Figure 2), on this repository:
https://github.com/declare-lab/awesome-sentiment-analysis.

Figure 2: The paper is logically divided into two sections. First, we analyze
the past trends and where we stand today in the sentiment analysis Literature.
Next, we present an Optimistic peek into the future of sentiment analysis,
where we discuss several applications and possible new directions. The red
bars in the figure estimates the present popularity of each application. The
lengths of these bars are proportional to the logarithm of the publication
counts on the corresponding topics in Google Scholar since 2000. Note: SA and
ABSA are the acronyms for Sentiment Analysis and Aspect-Based Sentiment
Analysis.

##  2 Nostalgic Past: Developments and Achievements in Sentiment Analysis

The fields of sentiment analysis and opinion mining — often used as synonyms —
aim to determine the sentiment polarity of unstructured content in the form of
text, audio streams, or multimedia-videos.

###  2.1 Early Sentiment Analysis

The task of sentiment analysis originated from the analysis of subjectivity in
sentences (Wiebe et al., 1999; Wiebe, 2000; Hatzivassiloglou & Wiebe, 2000; Yu
& Hatzivassiloglou, 2003; Wilson et al., 2005a). Wiebe (1994) associated
subjective sentences with private states of the speaker, that are not open for
observation or verification, taking various forms such as opinions or beliefs.
Research in sentiment analysis, however, became an active area only since 2000
primarily due to the availability of opinionated online resources (Tong, 2001;
Morinaga et al., 2002; Nasukawa & Yi, 2003). One of the seminal works in
sentiment analysis involves categorizing reviews based on their orientation
(sentiment) (Turney, 2002). This work generalized phrase-level orientation
mining by enlisting several syntactic rules (Hatzivassiloglou & McKeown, 1997)
and also introduced the bag-of-words concept for sentiment labeling. It stands
as one of the early milestones in developing this field of research.

Although preceded by related tasks, such as identifying affect, the onset of
the $21^{st}$ century marked the surge of modern-day sentiment analysis.

###  2.2 Granularities

Traditionally, sentiment analysis research has mainly focused on three levels
of granularity (Liu, 2012, 2010): document-level, sentence-level, and aspect-
level sentiment analysis.

In document-level sentiment analysis, the goal is to infer the overall opinion
of a document, which is assumed to convey a unique opinion towards an entity,
e.g., a product (Pang & Lee, 2004; Glorot et al., 2011; Moraes et al., 2013b).
Pang et al. (2002) conducted one of the initial works on document-level
sentiment analysis, where they assigned positive/negative polarity to review
documents. They used various features, including unigrams (bag of words) and
trained simple classifiers, such as Naive Bayes classifiers and SVMs. Although
primarily framed as a classification/regression task, alternate forms of
document-level sentiment analysis research include other tasks such as
generating opinion summaries (Ku et al., 2006; Lloret et al., 2009).

Sentence-level sentiment analysis restricts the analysis to individual
sentences (Yu & Hatzivassiloglou, 2003; Kim & Hovy, 2004). These sentences
could belong to documents, conversations, or standalone micro-texts found in
resources such as microblogs (Kouloumpis et al., 2011).

While both document- and sentence-level sentiment analysis provide an overall
sentiment orientation, they do not indicate the target of the sentiment in
many cases. They implicitly assume that the text span (document or sentence)
conveys a single sentiment towards an entity, which typically is a very strong
assumption.

To overcome this challenge, the analysis is directed towards a finer level of
scrutiny, i.e., aspect-level sentiment analysis, where sentiment is identified
for each entity (Hu & Liu, 2004b) (along with its aspects). Aspect-level
analysis allows a better understanding of the sentiment distribution. We
discuss its challenges in Section 3.1.

In addition to these three granularity levels, a significant amount of studies
have been done for phrase-level sentiment analysis, which focus on phrases
within a sentence (Wilson et al., 2005b). In this granularity, the goal is to
analyze how the sentiment of words, present in lexicons, can change in and out
of context, e.g., good vs. doesn’t look good. Many compositional factors, such
as negators, modals, or intensifiers, may flip or change the degree of
sentiment (Kiritchenko et al., 2016), which makes it a difficult yet important
task.

In the initial developments of sentiment analysis, phrase-level study
contributed numerous advances in defining rules that accounted for these
compositions. We discuss these in the next section. Phrase-level sentiment
analysis has also been important in small text pieces found in micro-blogs,
such as Twitter. Many recent shared-tasks discuss this area of research (Nakov
et al., 2013; Rosenthal et al., 2014, 2015) (see Section 2.4).

###  2.3 Trends in Sentiment Analysis Applications

Figure 3: A non-exhaustive illustration of some of the milestones of sentiment
analysis research.

##### Rule-Based Sentiment Analysis

A major section of the history of sentiment analysis research has focused on
utilizing sentiment-bearing words and utilizing their compositions to analyze
phrasal units for polarity. Early work identified that the simple counting of
valence words, i.e., a bag-of-words approach, can provide incorrect results
(Polanyi & Zaenen, 2006). This led to the emergence of research on valence
shifters that incorporated changes in valence and polarity of terms based on
contextual usage (Polanyi & Zaenen, 2006; Moilanen & Pulman, 2007). However,
only valence shifters were not enough to detect sentiment – it also required
understanding sentiment flows across syntactic units. Thus, researchers
introduced the concept of modeling sentiment composition, learned via
heuristics and rules (Choi & Cardie, 2008), hybrid systems (Rentoumi et al.,
2010), syntactic dependencies (Nakagawa et al., 2010; Hutto & Gilbert, 2014),
amongst others.

Sentiment Lexicons are at the heart of rule-based sentiment analysis methods.
Simply defined, these lexicons are dictionaries that contain sentiment
annotations for their constituent words, phrases, or synsets (Joshi et al.,
2017a; Cambria et al., 2020).

SentiWordNet (Esuli & Sebastiani, 2006) is one such popular sentiment lexicon
that builds on top of Wordnet (Miller, 1995). In this lexicon, each synset is
assigned with positive, negative, and objective scores, which indicate their
subjectivity orientation. As the labeling is associated with synsets, the
subjectivity score is tied to word senses. This trait is desirable as
subjectivity and word-senses have strong semantic dependence, as highlighted
in Wiebe & Mihalcea (2006).

SO-CAL (Taboada et al., 2011), as the name suggests, presents a lexicon-based
sentiment calculator. It contains a dictionary of words associated with their
semantic orientation (polarity and strength). The strength of this resource is
in its ability to account for contextual valence shifts, which include factors
that affect the polarity of words through intensification, lessening, and
negations.

Other popular lexicons include SCL-OPP (Kiritchenko & Mohammad, 2016a), SCL-
NMA (Kiritchenko & Mohammad, 2016b), amongst others. These lexicons not just
store word-polarity associations but also include phrases or rules that
reflect complex sentiment compositions, e.g., negations, intensifiers. NRC
Lexicon Mohammad & Turney (2010, 2013) is another popular lexicon that hosts
both word-sentiment and word-emotion pairs with high-quality annotations.

Lexicons have been mainly created using three broad approaches, manual,
automatic, and semi-supervised. In manual creation, expert-annotation (Taboada
et al., 2011) or crowd-sourcing (Socher et al., 2013) is used to create
annotations. In automatic methods, annotations are generated or expanded using
knowledge bases, such as Wordnet. The advantage of automatic methods is a
broader coverage of instances and also sense-specific annotations, especially
for Wordnet-based expansions (Esuli & Sebastiani, 2006). However, automatic
methods tend to have higher noise in the annotations. The final approach is to
use semi-supervised learning, such as label propagation (Zhu & Ghahramani,
2002) or graph propagation (Velikovich et al., 2010), to create new sentiment
lexicons that could be domain-specific (Tai & Kao, 2013) or in new languages
(Chen & Skiena, 2014) – two topics that hold high relevance in present
research directions.

Though lexicons provide valuable resources for archiving sentiment polarity of
words or phrases, utilizing them to infer sentence-level polarities has been
quite challenging. Moreover, no one lexicon can handle all the nuances
observed from semantic compositionality (Toledo-Ronen et al., 2018;
Kiritchenko & Mohammad, 2016d) or account for contextual polarity. Lexicons
also have many challenges in their creation, such as combating subjectivity in
annotations (Mohammad, 2017).

While sentiment lexicons remain an integral component of sentiment analysis
systems, especially for low-resource instances, there has been an increase in
focus towards statistical solutions. These solutions do not suffer from the
issue of rules-coverage and provide better opportunities to handle
generalization.

##### Machine Learning-Based Sentiment Analysis

Statistical approaches that employ machine learning have been appealing to
this area, particularly due to their independence over hand-engineered rules.
Despite best efforts, the rules could never be enumerated exhaustively, which
always kept the generalization capability limited. With machine learning, the
opportunity to learn generic representations emerged. Throughout the
development of sentiment analysis, ML-based approaches–both supervised and
unsupervised–have employed myriad of algorithms that include SVMs (Moraes et
al., 2013a), Naive Bayes Classifiers (Tan et al., 2009), nearest neighbour
(Moghaddam & Ester, 2010), combined with features that range from bag-of-words
(including weighted variants) (Martineau & Finin, 2009), lexicons (Gavilanes
et al., 2016) to syntactic features such as parts of speech (Mejova &
Srinivasan, 2011). A detailed review for most of these works has been provided
in (Liu, 2010, 2012).

##### Deep Learning Era

The advent of deep learning saw the use of distributional embeddings and
techniques for representation learning for various tasks of sentiment
analysis. One of the initial models was the Recursive Neural Tensor Network
(RNTN) Socher et al. (2013), which determined the sentiment of a sentence by
modeling the compositional effects of sentiment in its phrases. This work also
introduced the Stanford Sentiment Treebank corpus comprising of parse trees
fully labeled with sentiment labels. The unique usage of recursive neural
networks adapted to model the compositional structure in syntactic trees was
highly innovative and influencing (Tai et al., 2015).

CNNs and RNNs were also used for feature extraction. The popularity of these
networks, especially that of CNNs, can be traced back to Kim (2014). Although
CNNs had been used in NLP systems earlier (Collobert et al., 2011), the
investigatory work by Kim (2014) presented a CNN architecture which was simple
(single-layered) and also delved into the notion of non-static embeddings. It
was a popular network, that became the de-facto sentential feature extractor
for many of the sentiment analysis tasks. Similar to CNNs, RNNs also enjoyed
high popularity. Aside from polarity prediction, these architectures
outperformed traditional graphical models in structured prediction tasks such
as aspect, aspect-term and opinion-term extraction (Poria et al., 2016; Irsoy
& Cardie, 2014). Aspect-level sentiment analysis, in particular, saw an
increase in complex neural architectures that involve attention mechanisms
(Wang et al., 2016), memory networks (Tang et al., 2016b) and adversarial
learning (Karimi et al., 2020; Chen et al., 2018). For a comprehensive review
of modern deep learning architectures, please refer to (Zhang et al., 2018a).

Although the majority of the works employing deep networks rely on automated
feature learning, their heavy reliance on annotated data is often limiting. As
a result, providing inductive biases via syntactic information, or external
knowledge in the form of lexicons as additional input has seen a resurgence
(Tay et al., 2018b).

As seen in Figure 1, the recent works based on neural architectures (Le &
Mikolov, 2014; Dai & Le, 2015; Johnson & Zhang, 2016; Miyato et al., 2017;
McCann et al., 2017; Howard & Ruder, 2018; Xie et al., 2019; Thongtan &
Phienthrakul, 2019) have dominated over traditional machine learning models
(Maas et al., 2011; Wang & Manning, 2012). Similar trends can be observed in
other benchmark datasets such as Yelp, SST (Socher et al., 2013), and Amazon
Reviews (Zhang et al., 2015). Within neural methods, much like other fields of
NLP, present trends are dominated by the contextual encoders, which are pre-
trained as language models using the Transformer architecture (Vaswani et al.,
2017). Models like BERT, XLNet, RoBERTa, and their adaptations have achieved
the state-of-the-art performances on multiple sentiment analysis datasets and
benchmarks (Hoang et al., 2019; Munikar et al., 2019; Raffel et al., 2019).
Despite this progress, it is still not clear as to whether these new models
learn the composition semantics associated to sentiment or simply learn
surface patterns (Rogers et al., 2020).

##### Sentiment-Aware Word Embeddings

One of the critical building blocks of a text-processing deep-learning
architecture is its word embeddings. It is known that the word representations
rely on the task it is being used for (Labutov & Lipson, 2013). However, most
sentiment analysis-based models use static general-purpose word
representations. Tang et al. (2014) proposed an important work in this
direction that provided word representations tailored for sentiment analysis.
While general embeddings mapped words with similar syntactic context into
nearby representations, this work incorporated sentiment information into the
learning loss to account for sentiment regularities. Although the community
has proposed some approaches in this topic (Maas et al., 2011; Bespalov et
al., 2011), promising traction has been limited (Tang et al., 2015). Further,
with the popularity of contextual models such as BERT, it remains to be seen
how sentiment information can be incorporated into its embeddings.

Figure 4: The example illustrates the various challenges and applications that
holistic sentiment analysis depends on.

###  2.4 Sentiment Analysis in Diverse Domains

Sentiment analysis in micro-blogs, such as Twitter, require different
processing techniques compared to traditional text pieces. Enforced maximum
text length often coerces users to express their opinion straightforwardly.
However, sarcasm and irony pose a challenge to these systems. Tweets are rife
with internal slangs, abbreviations, and emoticons – which adds to the
complexity of mining their opinions. Moreover, the limited length restricts
the presence of contextual cues normally present in dialogues or documents
(Kharde & Sonawane, 2016).

From a data point of view, opinionated data is found in abundance in such
micro-blogs. Reflections of this have been observed in the recent benchmark
shared tasks based on Twitter data. These include Semeval shared tasks for
sentiment analysis, aspect-based sentiment analysis and figurative language in
Twitter 111http://alt.qcri.org/semeval2015/task10/,
222http://alt.qcri.org/semeval2015/task12/,
333http://alt.qcri.org/semeval2015/task11/,
444https://sites.google.com/view/figlang2020/.

A new trend amongst users in Twitter is the concept of daisy-chaining multiple
tweets to compose a longer piece of text. Existing research, however, has not
addressed this phenomenon to acquire additional context. Future work on
Twitter sentiment analysis could benefit from analyzing users’ personalities
based on their historical tweets.

The application of sentiment analysis is not limited to social media and
review articles. It spans from emails (Hag Ali & El Gayar, 2019) to financial
documents (Krishnamoorthy, 2017) showing the efficacy in understanding the
mood of users across different domains.

##  3 Optimistic Future: Upcoming Trends in sentiment analysis

The previous section highlighted some of the milestones in sentiment analysis
research, which helped develop the field into its present state. Despite the
progress, we believe the problems are far from solved. In this section, we
take an optimistic view on the road ahead in sentiment analysis research and
highlight several applications rife with open problems and challenges.

Applications of sentiment analysis take form in many ways. Fig. 4 presents one
such example where a user is chatting with a chit-chat style chatbot. In the
conversation, to generate an appropriate response, the bot needs to understand
the user’s opinion. This involves multiple sub-tasks that include 1)
extracting aspects like service, seats for the entity airline, 2) aspect-level
sentiment analysis along with knowing 3) who holds the opinion and why
(sentiment reasoning). Added challenges include analyzing code-mixed data
(e.g. “les meilleurs du monde”), understanding domain-specific terms (e.g.,
rude crew), and handling sarcasm – which could be highly contextual and
detectable only when preceding utterances are taken into consideration. Once
the utterances are understood, the bot can now determine appropriate response-
styles and perform controlled-natural language generation (NLG) based on the
decided sentiment. The overall example demonstrates the dependence of
sentiment analysis on these applications and sub-tasks, some of which are new
and still at early development stages. We discuss these applications next.

###  3.1 Aspect-Based Sentiment Analysis

Although sentiment analysis provides an overall indication of the author or
speaker’s sentiments, it is often the case when a piece of text comprises
multiple aspects with varied sentiments. For example, in the following
sentence, “ _This actor is the only failure in an otherwise brilliant cast._
”, the opinion is attached to two particular entities, actor (negative
opinion) and cast (positive opinion). There is also an absence of an overall
opinion that could be assigned to the full sentence.

Aspect-based Sentiment Analysis (ABSA) takes such fine-grained view and aims
to identify the sentiments towards each entity (or their aspects) (Liu, 2015;
Liu & Zhang, 2012). The problem involves two major sub-tasks, 1) Aspect-
extraction, which identifies the aspects 555In the context of aspect-based
sentiment analysis, aspect is the generic term utilized for topics, entities,
or their attributes/features. They are also known as opinion targets.
mentioned within a given sentence or paragraph (actor and cast in the above
example) 2) Aspect-level Sentiment Analysis (ALSA), which determines the
sentiment orientation associated with the corresponding aspects/ opinion
targets (actor $\mapsto$ negative and cast $\mapsto$ positive) (Hu & Liu,
2004a). Proposed approaches for aspect extraction include rule-based
strategies (Qiu et al., 2011; Liu et al., 2015), topic models (Mei et al.,
2007; He et al., 2011), and more recently, sequential structured-prediction
models such as CRFs (Shu et al., 2017). For aspect-level sentiment analysis,
the algorithms primarily aim to model the relationship between the opinion
targets and their context. To achieve this, models based on CNNs (Li & Lu,
2017), memory networks (Tay et al., 2017), etc. have been explored. Primarily,
the associations have been learnt through attention mechanism (Wang et al.,
2016).

Despite the advances in this field, there remain many factors which are open
for research and hold the potential to improve performances further. We
discuss them below.

Figure 5: An example of the aspect term auto-categorization.

####  3.1.1 Aspect-Term Auto-Categorization

Aspect-terms extraction is the first step towards aspect-level sentiment
analysis. This task has been studied rigorously in the literature (Poria et
al., 2016). Thanks to the advent of deep sequential learning, the performance
of this task on the benchmark datasets (Hu & Liu, 2004b; Pontiki et al., 2016)
has reached a new level. Aspect terms are needed to be categorized into aspect
groups to present a coherent view of the expressed opinions. We illustrate
this categorization in Fig. 5. Approaches to aspect-term auto-categorization
are mostly based on supervised and unsupervised topic classification and
lexicon driven. These three types of approaches succumb to scalability issues
when subjected to new domains with novel aspect categories. We believe that
entity linking-based approaches, coupled with semantic graphs like Probase (Wu
et al., 2012), should be able to perform reasonably while overcoming
scalability issues. For example, the sentence “ _With this phone, I always
have a hard time getting signal indoors._” contains one aspect term _signal_ ,
that can be passed to an entity linker — on a graph containing a tree shown in
Fig. 5 — with the surrounding words as context to obtain aspect category
_phone:signal-quality_.

####  3.1.2 Implicit Aspect-Level Sentiment Analysis

Sentiment on different aspects can be expressed implicitly. Although under-
studied, the importance of detecting implicit aspect-level sentiment can not
be ignored as they represent a unique nature of natural language. For example,
in the sentence, “Oh no! Crazy Republicans voted against this bill”, the
speaker explicitly expresses her/his negative sentiment on the _Republicans_.
At the same time, we can infer that the speaker’s sentiment towards the _bill_
is positive. In the work by Deng et al. (2014), it is called as _opinion-
oriented implicatures_. As most datasets are not labeled with such details,
models trained on present datasets might miss extracting _bill_ as an aspect
term and its associated polarity. Thus, this remains an open problem in the
overall ABSA task. The case of implicit sentiment is also observed in
contextual sentiment analysis, which we further discuss in Section 3.3.2.

####  3.1.3 Aspect Term-Polarity Co-Extraction

Most existing algorithms in this area consider aspect extraction and aspect-
level sentiment analysis as sequential (pipelined) or independent tasks. In
both these cases, the relationship between the tasks is ignored. Efforts
towards joint learning of these tasks have gained traction in recent trends.
These include hierarchical neural networks (Lakkaraju et al., 2014), multi-
task CNNs (Wu et al., 2016), and CRF-based approaches by framing both the sub-
tasks as sequence labeling problems (Li et al., 2019; Luo et al., 2019). The
notion of joint learning opens up several avenues for exploring the
relationships between the sub-tasks and possible dependencies from other
tasks.

Another strategy is to leverage transfer learning since aspect extraction can
be utilized as a scaffolding for aspect-based sentiment analysis (Majumder et
al., 2020). Knowledge transfer can also be observed from textual to multimodal
ABSA system.

####  3.1.4 Exploiting Inter-Aspect Relations for Aspect-Level Sentiment
Analysis

The primary focus of algorithms proposed for aspect-level sentiment analysis
has been to model the dependencies between opinion targets and their
corresponding opinionated words in the context (Tang et al., 2016a). Besides,
modeling the relationships between aspects also holds potential in this task
(Hazarika et al., 2018c). For example, in the sentence ”my favs here are the
tacos pastor and the tostada de tinga”, the aspects _”tacos pastor”_ and
_”tostada de tinga”_ are connected using conjunction _”and”_ and both rely on
the sentiment bearing word _”favs”_. Understanding such inter-aspect
dependency can significantly aid the aspect-level sentiment analysis
performance and remains to be researched extensively.

####  3.1.5 Quest for Richer and Larger Datasets

The two widely used publicly available datasets for aspect-based sentiment
analysis are Amazon product review (Hu & Liu, 2004b) and Semeval-2016 (Pontiki
et al., 2016) datasets. Both these datasets are quite small in size that
hinders any statistically significant performance improvement between methods
utilizing them.

###  3.2 Multimodal Sentiment Analysis

The majority of research works on sentiment analysis have been conducted using
only textual modality. However, with the increasing number of user-generated
videos available on online platforms such as YouTube, Facebook, Vimeo, and
others, multimodal sentiment analysis has emerged at the forefront of
sentiment analysis research. The commercial interests fuel this rise as the
enterprises tend to make business decisions on their products by analyzing
user sentiments in these videos. Figure 6 presents examples where the presence
of multimodal signals in addition to the text itself is necessary in order to
make correct predictions of their emotions and sentiments. Multimodal fusion
is at the heart of multimodal sentiment analysis, with an increasing number of
works proposing new fusion techniques. These include Multiple Kernel Learning,
tensor-based non-linear fusion (Zadeh et al., 2017), memory networks (Zadeh et
al., 2018a), amongst others. The granularity at which such fusion methods are
applied also varies – from word-level to utterance-level.

Figure 6: Importance of multimodal cues. Green shows primary modalities
responsible for sentiment and emotion.

Below, we identify three key directions that can aid future research:

####  3.2.1 Complex Fusion Methods vs. Simple Concatenation

Multimodal information fusion is a core component of multimodal sentiment
analysis. Although several fusion techniques (Zadeh et al., 2018c, a, 2017)
have been recently proposed, in our experience, a simple concatenation-based
fusion method performs at par with most of these methods. We believe these
methods are unable to provide significant improvements in the fusion due to
their inability to model correlations among different modalities and handle
noise. Reliable fusion remains a major future work.

####  3.2.2 Lack of Large Datasets

The field of multimodal sentiment analysis also suffers from the lack of
larger datasets. The available datasets, such as MOSI (Zadeh et al., 2016),
MOSEI (Zadeh et al., 2018b), MELD (Poria et al., 2019) are not large enough
and carry suboptimal inter-annotator agreement that impedes the performance of
complex deep learning frameworks.

####  3.2.3 Fine-Grained Annotation

The primary goal of multimodal fusion is to accumulate the contribution from
each modality. However, measuring that contribution is not trivial as there is
no available dataset that annotates the individual role of each modality. We
show one such example in Figure 6, where each modality is labeled with the
sentiment it carries. Having such rich fine-grained annotations should better
guide multimodal fusion methods and make them more interpretable. This fine-
grained annotation can also open the door to novel multimodal fusion
approaches.

###  3.3 Contextual Sentiment Analysis

####  3.3.1 Influence of Topics

The usage of sentiment words varies from one topic to another. Words that
sound neutral on the surface can bear sentiment when conjugated with other
words or phrases. For example, the word _big_ in _big house_ can carry
positive sentiment when someone intends to purchase a _big house_ for leisure.
However, the same word could evoke negative sentiments when used in the
context – _A big house is hard to clean_. Unfortunately, research in sentiment
analysis has not focused much on this aspect. The sentiment of some words can
be vague and specified only when seen in context, e.g., the word _massive_ in
the context of _massive earthquake_ and _massive villa_. A dataset composed of
such contextual sentiment bearing phrases would be a great contribution to the
research community in the future.

This research problem is also related to word sense disambiguation. Below we
present an example, borrowed from the work by Choi et al. (2017):

  1. a.

The Federal Government _carried_ the province for many years.

  2. b.

The troops _carried_ the town after a brief fight.

In the first sentence, the sense of _carry_ has a positive polarity. However,
in the second sentence, the same word has negative connotations. Hence,
depending on the context, the sense of words and their polarities can change.
In Choi et al. (2017), the authors adopted topic models to associate word
senses with sentiments. As this particular research problem widens its scope
to the task of word sense disambiguation, it would be useful to employ
contextual language models to decipher word senses in contexts and assign the
corresponding polarity.

####  3.3.2 Sentiment Analysis in Monologues and Conversational Context

Context is at the core of NLP research. According to several recent studies
(Peters et al., 2018; Devlin et al., 2019), contextual sentence and word-
embeddings can improve the performance of the state-of-the-art NLP systems by
a significant margin.

The notion of context can vary from problem to problem. For example, while
calculating word representations, the surrounding words carry contextual
information. Likewise, to classify a sentence in a document, other neighboring
sentences are considered as its context. Poria et al. (2017) utilize
surrounding utterances in a video as context and experimentally show that
contextual evidence indeed aids in classification.

Figure 7: An abridged dialogue from the IEMOCAP dataset (Busso et al., 2008).

There have been very few works on inferring implicit sentiment (Deng & Wiebe,
2014) from context. This is crucial for achieving true sentiment
understanding. Let us consider this sentence “ _Oh no. The bill has been
passed_ ”. As there are no explicit sentiment markers present in the isolated
sentence – _“The bill has been passed”_ , it would sound like a neutral
sentence. Consequently, the sentiment behind ‘bill’ is not expressed by any
particular word. However, considering the sentence in the context – _“Oh no”_
, which exhibits negative sentiment, it can be inferred that the opinion
expressed on the ‘bill’ is negative. The inferential logic that one requires
to arrive at such conclusions is the understanding of sentiment flow in the
context. In this particular example, the contextual sentiment of the sentence
– “Oh no” flows to the next sentence and thus making it a negative opinionated
sentence. Tackling such tricky and fine-grained cases require bespoke modeling
and datasets containing an ample quantity of such non-trivial samples.
Further, commonsense knowledge can also aid in making such inferences. In the
literature (Poria et al., 2017), the use of LSTMs to model such sequential
sentiment flow has been ineffectual. We think it would be fruitful to utilize
logic rules, finite-state transducers, belief, and information propagation
mechanisms to address this problems (Deng et al., 2014; Deng & Wiebe, 2014).
We also note that contextual sentences may not always help. Hence, one can
ponder using a gate or switch to learn and further infer when to count on
contextual information.

In conversational sentiment-analysis, to determine the emotions and sentiments
of an utterance at time $t$, the preceding utterances at time $<t$ can be
considered as its context. However, computing this context representation can
often be difficult due to complex sentiment dynamics.

Sentiments in conversations are deeply tied with emotional dynamics consisting
of two important aspects: self- and inter-personal dependencies (Morris &
Keltner, 2000). Self-dependency, also known as emotional inertia, deals with
the aspect of influence that speakers have on themselves during conversations
(Kuppens et al., 2010). On the other hand, inter-personal dependencies relate
to the sentiment-aware influences that the counterparts induce into a speaker.
Conversely, during the course of a dialogue, speakers also tend to mirror
their counterparts to build rapport (Navarretta et al., 2016). This phenomenon
is illustrated in Figure 7. Here, $P_{a}$ is frustrated over her long term
unemployment and seeks encouragement ($u_{1},u_{3}$). $P_{b}$, however, is
pre-occupied and replies sarcastically ($u_{4}$). This enrages $P_{a}$ to
appropriate an angry response ($u_{6}$). In this dialogue, self-dependencies
are evident in $P_{b}$, who does not deviate from his nonchalant behavior.
$P_{a}$, however, gets sentimentally influenced by $P_{b}$. Modeling self- and
inter-personal relationships and dependencies may also depend on the topic of
the conversation as well as various other factors like argument structure,
interlocutors’ personality, intents, viewpoints on the conversation, attitude
towards each other, and so on. Hence, analyzing all these factors is key for a
true self and inter-personal dependency modeling that can lead to enriched
context understanding (Hazarika et al., 2018d).

The contextual information can come from both local and distant conversational
history. As opposed to the local context, distant context often plays a
smaller role in sentiment analysis of conversations. Distant contextual
information is useful mostly in the scenarios when a speaker refers to earlier
utterances spoken by any of the speakers in the conversational history.

Figure 8: Role of context in sentiment analysis in conversation.

The usefulness of context is more prevalent in classifying short utterances,
like yeah, okay, no, that can express different sentiments depending on the
context and discourse of the dialogue. The examples in Fig. 8 explain this
phenomenon. The sentiment expressed by the same utterance “Yeah” in both these
examples differ from each other and can only be inferred from the context.

Leveraging such contextual clues is a difficult task. Memory networks, RNNs,
and attention mechanisms have been used in previous works, e.g., HRLCE (Huang
et al., 2019a) or DialogueRNN (Majumder et al., 2019a), to grasp information
from the context. However, these models fail to explain the situations where
contextual information is needed. Hence, finding contextualized conversational
utterance representations is an active area of research.

####  3.3.3 User, Cultural, and Situational Context

Sentiment also depends on the user, cultural, and situational context.

Individuals have subtle ways of expressing emotions and sentiments. For
instance, some individuals are more sarcastic than others. For such cases, the
usage of certain words would vary depending on if they are being sarcastic.
Let’s consider this example, $P_{a}:$ The order has been cancelled., $P_{b}:$
This is great!. If $P_{b}$ is a sarcastic person, then his response would
express negative emotion to the order being canceled through the word great.
On the other hand, $P_{b}$’s response, great, could be taken literally if the
canceled order is beneficial to $P_{b}$ (perhaps $P_{b}$ cannot afford the
product he ordered). As necessary background information is often missing from
the conversations, speaker profiling based on preceding utterances often
yields improved results.

The underlying emotion of the same word can vary from one person to another.
E.g., the word _okay_ can bear different sentiment intensity and polarity
depending on the speaker’s character. This incites the need to do user
profiling for fine-grained sentiment analysis, which is a necessary task for
e-commerce product review understanding.

Understanding sentiment also requires cultural and situational awareness. A
hot and sunny weather can be treated as a good weather in USA but certainly
not in Singapore. Eating ham could be accepted in one religion and prohibited
by another.

A basic sentiment analysis system that only relies on distributed word
representations and deep learning frameworks are susceptible to these examples
if they do not encompass rudimentary contextual information.

####  3.3.4 Role of Commonsense Knowledge in Sentiment Analysis

In layman’s term, commonsense knowledge consists of facts that all human
beings are expected to know. Due to this characteristic, humans tend to ignore
expressing commonsense knowledge explicitly. As a result, word embeddings
trained on the human-written text do not encode such trivial yet important
knowledge that can potentially improve language understanding. The
distillation of commonsense knowledge, thus, has become a new trend in modern
NLP research. We show one such example in the Fig. 9 which illustrates the
latent commonsense concepts that humans easily infer or discover given a
situation. In particular, the present scenario informs that David is a good
cook and will be making pasta for some people. Based on this information,
commonsense can be employed to infer related events such as, dough for the
pasta would be available, people would eat food (pasta), the pasta is expected
to be good (David is good cook), etc. These inferences would enhance the text
representation with many more concepts that can be utilized by neural systems
in diverse downstream tasks.

Figure 9: An illustration of commonsense reasoning and inference. Figure 10:
Sentiment cause analysis.

In the context of sentiment analysis, utilizing commonsense for associating
aspects with their sentiments can be highly beneficial for this task.
Commonsense knowledge graphs connect the aspects to various sentiment-bearing
concepts via semantic links (Ma et al., 2018). Additionally, semantic links
between words can be utilized to mine associations between the opinion target
and the opinion-bearing word. What is the best way to grasp commonsense
knowledge is still an open research question.

Commonsense knowledge is also required to understand _implicit_ sentiment of
the sentences that do not accommodate any explicit sentiment marker. E.g., the
sentiment of the speaker in this sentence, _“We have not seen the sun since
last week”_ is negative as not catching the sight of the sun for a long time
is generally treated as a negative event in our society. A system not adhering
to this commonsense knowledge would fail to detect the underlying sentiment of
such sentences correctly.

With the advent of commonsense modeling algorithms such as Comet (Bosselut et
al., 2019), we think, there will be a new wave of research focusing on the
role of commonsense knowledge in sentiment analysis in the near future.

###  3.4 Sentiment Reasoning

Apart from exploring the what, we should also explore the who and why. Here,
the who detects the entity whose sentiment is being determined, whereas why
reveals the stimulus/reason for the sentiment.

####  3.4.1 Who? The Opinion Holder

While analyzing opinionated text, it is often important to know the opinion
holder. In most cases, the opinion holder is the person who spoke/wrote the
sentence. Yet, there can be situations where the opinion holder is an entity
(or entities) mentioned in the text (Mohammad, 2017). Consider the following
two lines of opinionated text:

  1. a.

The movie was too slow and boring.

  2. b.

Stella found the movie to be slow and boring.

In both the sentences above, the sentiment attached to the movie is negative.
However, the opinion holder for the first sentence is the speaker while in the
second sentence it is Stella. The task could be further complex with the need
to map varied usage of the same entity term (e.g. Jonathan, John) or the use
of pronouns (he, she, they) (Liu, 2012).

Many works have studied the task of opinion-holder identification – a subtask
of opinion extraction (opinion holder, opinion phrase, and opinion target
identification). These works include approaches that use named-entity
recognition (Kim & Hovy, 2004), parsing and ranking candidates (Kim & Hovy,
2006), semantic role labeling (Wiegand & Ruppenhofer, 2015), structured
prediction using CRFs (Choi et al., 2006), multi-tasking (Yang & Cardie,
2013), amongst others. The MPQA corpus (Deng & Wiebe, 2015) provided
supervised annotations for this task. However, for deep learning approaches,
this topic has been understudied (Zhang et al., 2019a; Quan et al., 2019).

####  3.4.2 Why? The Sentiment Stimulus

The majority of the sentiment analysis research works to date are about
classifying contents into positive, negative, and neutral and till date, only
a little attention has been paid to sentiment cause identification. Future
research in sentiment analysis should focus on what drives a person to express
positive or negative sentiment on a topic or aspect.

To reason about a particular sentiment of an opinion-holder, it is important
to understand the target of the sentiment (Deng & Wiebe, 2014), and whether
there are implications of holding such sentiment. For instance, when stating
“I am sorry that John Doe went to prison.”, understanding the target of the
sentiment in the phrase ”John Doe goes to prison”, and knowing that “go to
prison” has negative implications on the target, implies positive sentiment
toward John Doe.666Example provided by Jan Wiebe (2016), personal
communication. Moreover, it is important to understand what caused the
sentiment. Although in this example, it is straightforward to conclude that
“go to prison” is the reason of the expressed negative expression. One can
also infer new knowledge from this simplified reasoning — “go to prison” is a
negative event. This sentiment knowledge discovery can further help to enrich
phrase-level sentiment lexicons.

The ability to reason is necessary for any explainable AI system. In the
context of sentiment analysis, it is often desired to understand the cause of
an expressed sentiment by the speaker. E.g., consider a review on a
smartphone, “I hate the touchscreen as it freezes after 2-3 touches”. While it
is critical to detect the negative sentiment expressed on _touchscreen_ ,
digging into the detail that causes this sentiment is also of prime importance
(Liu, 2012), which in this case is implied by the phrase “freezes after 2-3
touches”. To date, there is not much work exploring this aspect of the
sentiment analysis research. Li & Hovy (2017) discuss two possible reasons
that give rise to opinions. Firstly, an opinion-holder might have an emotional
bias towards the entity/topic in question. Secondly, the sentiment could be
borne out of mental (dis)satisfaction towards a goal achievement.

Grasping the cause of sentiment is also very important in dialogue systems. As
an example, we can refer to Figure 10, where Joey expresses _anger_ once he
ascertains Chandler’s _deception_ in the previous utterance.

It is hard to define a taxonomy or tagset for the reasoning of both emotions
and sentiments. At present, there is no available dataset that contains such
rich annotations. Building such a dataset would enable future dialogue systems
to frame meaningful argumentation logic and discourse structure, taking one
step closer to human-like conversation.

###  3.5 Domain Adaptation

Most of the state-of-the-art sentiment analysis models enjoy the privilege of
having in-domain training datasets. However, this is not a viable scenario, as
curating large amounts of training data for every domain is impractical.
Domain adaptation in sentiment analysis solves this problem by learning the
characteristics of the unseen domain. Sentiment classification, in fact, is
known to be sensitive towards domains as mode of expressing opinions across
domains vary. Also, valence of affective words may vary based on different
domains (Liu, 2012).

Diverse approaches have been proposed for cross-domain sentiment analysis. One
line of work models domain-dependent word embeddings (Sarma et al., 2018; Shi
et al., 2018; K Sarma et al., 2019) or domain-specific sentiment lexicons
(Hamilton et al., 2016), while others attempt to learn representations based
on either co-occurrences of domain-specific with domain-independent terms
(pivots) (Blitzer et al., 2007; Pan et al., 2010; Ziser & Reichart, 2018;
Sharma et al., 2018) or shared representations using deep networks (Glorot et
al., 2011).

One of the major breakthroughs in domain adaptation research employs
adversarial learning that trains to fool a domain discriminator by learning
domain-invariant representations (Ganin et al., 2016). In this work, the
authors utilize bag of words as the input features to the network.
Incorporating bag of words limits the network to access any external knowledge
about the unseen words of the target domain. Hence, the performance
improvement can be completely attributed to the efficacy of the adversarial
network. However, in recent works, researchers tend to utilize distributed
word representations such as Glove, BERT. These representations, aka word
embeddings, are usually trained on huge open-domain corpora and consequently
contain domain invariant information. Future research should explain whether
the gain in domain adaptation performance comes from these word embeddings or
the core network architecture.

In summary, the works in domain adaptation lean towards outshining the state
of the art on benchmark datasets. What remains to be seen is the
interpretability of these methods. Although some works claim to learn the
domain-dependent sentiment orientation of the words during domain invariant
training, there is barely any well-defined analysis to validate such claims.

####  3.5.1 Use of External Knowledge

The key idea that most of the existing works encapsulate is to learn domain-
invariant shared representations as a means to domain adaptation. While global
or contextual word embeddings have shown their efficacy in modeling domain-
invariant and specific representations, it might be a good idea to couple
these embeddings with multi-relational external knowledge graphs for domain
adaptation. Multi-relation knowledge graphs represent semantic relations
between concepts. Hence, they can contain complementary information over the
word embeddings, such as Glove, since these embeddings are not trained on
explicit semantic relations. Semantic knowledge graphs can establish
relationships between domain-specific concepts of several domains using
domain-general concepts – providing vital information that can be exploited
for domain adaptation. One such example is presented in Fig. 11. Researchers
are encouraged to read these early works (Alam et al., 2018; Xiang et al.,
2010) on exploiting external knowledge for domain adaptation.

Figure 11: Domain-general term graphic bridges the semantic knowledge between
domain specific terms in Electronics, Books and DVD.

####  3.5.2 Scaling Up to Many Domains

Most of the present works in this area use the setup of a source and target
domain pair for training. Although appealing, this setup requires retraining
as and when the target domain changes. The recent literature in domain
adaptation goes beyond single-source-target (Zhao et al., 2018a) to multi-
source and multi-target (Gholami et al., 2020b, a) training. However, in
sentiment analysis, these setups have not been fully explored and deserve more
attention (Wu & Huang, 2016).

###  3.6 Multilingual Sentiment Analysis

The majority of sentiment analysis research has been conducted on English
datasets. However, the advent of social media platforms has made multilingual
content available via platforms such as Facebook and Twitter. Consequently,
there is a recent surge in works with diverse languages (Dashtipour et al.,
2016). The NLP community, in general, is now also vocal to promote research on
languages other than English.777Because of a now widely known statement made
by Professor Emily M.Bender on Twitter, we now use the term _#BenderRule_ to
require that the language addressed by research projects by explicitly stated,
even when that language is English https://bit.ly/3aIqS0C

In the context of sentiment analysis, despite the recent surge in multilingual
sentiment analysis, several directions need more traction:

####  3.6.1 Language-Specific Lexicons

Today’s rule-based sentiment analysis system, such as Vader, works great for
the English language, thanks to the availability of resources like sentiment
lexicons. For other languages such as Hindi, French, Arabic, not many well-
curated lexicons are available.

####  3.6.2 Sentiment Analysis of Code-Mixed Data

In many cultures, people on social media post content that are a mix of
multiple languages (Lal et al., 2019; Guptha et al., 2020; Gambäck & Das,
2016). For example, “Itna izzat diye aapne mujhe !!! Tears of joy. :’( :’(”,
in this sentence, the bold text is in Hindi with roman orthography, and the
rest is in English. Code-mixing poses a significant challenge to the rule- and
deep learning-based methods. A possible future work to combat this challenge
would be to develop language models on code-mixed data. How and where to mix
languages is a person’s own choice, one of the main hardships. Another
critical challenge associated with this task is identifying the deep
compositional semantic that lies in the code mixed data. Unfortunately, only a
little research has been carried out on this topic (Lal et al., 2019; Joshi et
al., 2016).

####  3.6.3 Machine Translation as a Solution to Multilingual Sentiment
Analysis

Can machine translation be used as a solution to multilingual or cross-lingual
sentiment analysis? Recently, several papers Saadany & Orasan (2020);
Balamurali et al. (2013) have attempted this problem. Saadany & Orasan (2020)
claim that the associated sentiment is not preserved for contronyms,
negations, diacritic, and idiomatic expressions when translated from Arabic to
English. One solution to tackle this problem, as proposed by Saadany & Orasan
(2020), is integrating sentiment information in the encoding stage in a
machine translation system. However, this research has yet to witness much
research attention.

###  3.7 Sarcasm Analysis

The study of sarcasm analysis is highly integral to the development of
sentiment analysis due to its prevalence in opinionated text (Maynard &
Greenwood, 2014; Majumder et al., 2019b). Detecting sarcasm is highly
challenging due to the figurative nature of text, which is accompanied by
nuances and implicit meanings (Jorgensen et al., 1984). Over recent years,
this research field has established itself as an important problem in NLP with
many works proposing different solutions to address this task (Joshi et al.,
2017b). Broadly, the main contributions have emerged from the speech and text
communities. In speech, existing works leverage different signals such as
prosodic cues (Bryant, 2010; Woodland & Voyer, 2011), acoustic features
including low-level descriptors, and spectral features (Cheang & Pell, 2008).
Whereas in textual systems, traditional approaches consider rule-based
(Khattri et al., 2015) or statistical patterns (González-Ibáñez et al.,
2011b), stylistic patterns (Tsur et al., 2010), incongruity (Joshi et al.,
2015; Tay et al., 2018a), situational disparity (Riloff et al., 2013), and
hashtags (Maynard & Greenwood, 2014). While stylistic patterns, incongruity,
and valence shifters are some of the ways humans use to express sarcasm, it is
also highly contextual. In addition, sarcasm also depends on a person’s
personality, intellect, and the ability to reason over commonsense. In the
literature, these aspects of sarcasm remain under-explored.

Figure 12: Incongruent modalities in sarcasm present in the MUStARD dataset
(Castro et al., 2019).

####  3.7.1 Leveraging Context in Sarcasm Detection

Although the research for sarcasm analysis has primarily dealt with analyzing
the sentence at hand, recent trends have started to acquire contextual
understanding by looking beyond the text. Similar to sentiment analysis (
Sections 3.2 and 3.3), sarcasm detection can benefit with contextual cues
provided by conversation histories, author tendencies, and multimodality.

##### User Profiling and Conversational Context

Two types of contextual information have been explored for providing
additional cues to detect sarcasm: authorial context and conversational
context. Leveraging authorial context delves with analyzing the author’s
sarcastic tendencies (user profiling) by looking at their historical and meta
data (Bamman & Smith, 2015; Hazarika et al., 2018a). Similarly, the
conversational context uses the additional information acquired from
surrounding utterances to determine whether a sentence is sarcastic (Ghosh et
al., 2018). It is often found that sarcasm is apparent only when put into
context over what was mentioned earlier. For example, when tasked to identify
whether the sentence “He sure played very well” is sarcastic, it is imperative
to look at prior statements in the conversation to reveal facts (“The team
lost yesterday”).

##### Multimodal Context

We also identify multimodal signals to be important for sarcasm detection.
Sarcasm is often expressed without linguistic markers, and instead, by using
verbal and non-verbal cues. Change of tone, overemphasis on words, straight
face, etc. are some such cues that indicate sarcasm. There have been very few
works that adopt multimodal strategies to determine sarcasm (Schifanella et
al., 2016). Castro et al. (2019) recently released a multimodal sarcasm
detection dataset, MUStARD, that takes conversational context into account.
Fig. 12 presents two cases from this dataset, where sarcasm is expressed
through the incongruity between modalities. In the first case, the language
modality indicates fear or anger. In contrast, the facial modality lacks any
visible sign of anxiety that would agree with the textual modality. In the
second case, the text is indicative of a compliment, but the vocal tonality
and facial expressions show indifference. In both cases, the incongruity
between modalities acts as a strong indicator of sarcasm. While useful,
MUStARD contains only 500 odd instances, posing a significant challenge to
training deep networks on this dataset.

####  3.7.2 Annotation Challenges: Intended vs. Perceived Sarcasm

Sarcasm is a highly subjective tool and poses significant challenges in
curating annotations for supervised datasets. This difficulty is particularly
evident in perceived sarcasm, where human annotators are employed to label
text as sarcastic or not. Sarcasm recognition is known to be a difficult task
for humans due to its reliance on pragmatic factors such as common ground
(Clark, 1996). This difficulty is also observed through the low annotator
agreements across the datasets curated for perceived sarcasm (González-Ibáñez
et al., 2011a; Castro et al., 2019). To combat such perceptual subjectivity,
recent emotion analysis approaches utilize perceptual uncertainty in their
modeling (Zhang et al., 2018b; Gui et al., 2017; Han et al., 2017).

In our experience of curating a multimodal sarcasm detection dataset (Castro
et al., 2019), we observed poor annotation quality, which occurred mainly due
to the hardships associated with this task. Hovy et al. (2013) noticed that
people undertaking such tasks remotely online are often guilty of spamming, or
providing careless or random responses.

One solution to this problem is to rely on self annotated data collection.
While convenient, obtaining labeled data from hashtags has been found to
introduce both noise (incorrectly-labeled examples) and bias (only certain
forms of sarcasm are likely to be tagged (Davidov et al., 2010), and
predominantly by certain types of Twitter users (Bamman & Smith, 2015)).

Recently, Oprea & Magdy (2019) presented the iSarcasm dataset, which provides
labels by the original writers for the sarcastic posts. This kind of
annotation is promising as it circumvents the issues mentioned above while
capturing the intended sarcasm. To address the issues stemmed from annotating
perceived sarcasm, Best-Worst Scaling (MaxDiff/BWS) (Kiritchenko & Mohammad,
2016c) could be employed to alleviate the effect of subjectivity in
annotations. BWS attempts to alleviate the ambiguity in annotations by asking
annotators to compare rather than indicate. Thus, rather than identifying the
presence of sarcasm or its intensity, BWS would ask annotators to choose the
most sarcastic (best) and least sarcastic (worst) sentences from candidate
4-tuples – leading to easier and better decision making. Kiritchenko &
Mohammad (2016c) shows that such comparisons can be converted into a ranked
list of the items based on the property of interest, which in this case is
perceived sarcasm.

####  3.7.3 Target Identification in Sarcastic Text

Identifying the target of ridicule within a sarcastic text – a new concept
recently introduced by Joshi et al. (2018) – has important applications. It
can help chat-based systems better understand user frustration and help ABSA
tasks assign the sarcastic intent with the correct target in general. Though
similar, there are differences from the vanilla aspect extraction task
(Section 3.1) as the text might contain multiple aspects/entities with only a
subset being a sarcastic target (Patro et al., 2019). When expressing sarcasm,
people tend not to use the target of ridicule explicitly, which makes this
task immensely challenging to combat.

####  3.7.4 Style Transfer between Sarcastic and Literal Meaning

##### Figurative to Literal Meaning Conversion

Converting a sentence from its figurative meaning to its honest and literal
form is an exciting application. It involves taking a sarcastic sentence such
as “I loved sweating under the sun the whole day” to “I hated sweating under
the sun the whole day”. It has the potential to aid opinion mining, sentiment
analysis, and summarization systems. These systems are often trained to
analyze the literal semantics, and such a conversion would allow for accurate
processing. Present approaches include converting a full sentence using
monolingual machine translation techniques (Peled & Reichart, 2017), and also
word-level analysis, where target words are disambiguated into their sarcastic
or literal meaning (Ghosh et al., 2015). This application could also help in
1) performing data augmentation and 2) generating adversarial examples as both
the forms (sarcastic and literal) convey the same meaning but with different
lexical forms.

##### Generating Sarcasm from Non-Figurative Sentences

The ability to generate sarcastic sentences is an important yardstick in the
development of NLG. The goal of building socially-relevant and engaging,
interactive systems demand such creativity. Sarcastic content generation can
also benefit content/media generation that finds applications in fields like
advertisements. Mishra et al. (2019b) recently proposed a modular approach to
generate sarcastic text from negative sentiment-aware scenarios. End-to-end
counterparts to this approach have not been well studied yet. Also, most of
the works here rely on a particular type of sarcasm – one which involves
incongruities within the sentence. The generation of other flavors of sarcasm
(as mentioned before) has not been yet studied. Recently, Chakrabarty et al.
(2020) proposed a retrieval-based method that focuses on some of the points
raised above, indicating the promise of research in this direction.

####  3.7.5 Sentiment and Creative Language

While the above discussion is focused on sarcasm analysis, other forms of
creative language tools, such as irony, humor, etc. also have co-dependence
with sentiment analysis. Irony is more generic than sarcasm as it used to mean
the opposite of what is being said, whereas sarcasm has an intent to
criticize. Previous works have utilized sentiment information for detecting
irony Farías et al. (2015). However, compared to sarcasm, research utilizing
sentiment-irony relationships has been scanty. Humor is another tool that is
often used in human language. Identifying humor also has deep ties with
sentiment analysis. Multiple works mention that detecting humor often relies
on mining the sentimental relations between the setup and the corresponding
punchline Liu et al. (2018); Hasan et al. (2019); Zhang et al. (2019b). Recent
works also reveal that architectures developed for sentiment analysis perform
similarly for tasks related to humor prediction Hazarika et al. (2020).
Looking at the reverse, some works demonstrate that knowing humor, sarcasm,
etc. aids in making sentiment analysis systems more robust Badlani et al.
(2019). The above points indicate the relationships between sentiment analysis
with creative language, thus opening whole new doors for research Majumder et
al. (2019b).

###  3.8 Sentiment-Aware Natural Language Generation (NLG)

Language generation is considered one of the major components of the field of
NLP. Historically, the focus of statistical language models has been to create
syntactically coherent text using architectures such as n-grams models
(Stolcke, 2002) or auto-regressive recurrent architectures (Bengio et al.,
2003; Mikolov et al., 2010; Sundermeyer et al., 2012). These generative models
have important applications in areas including representation learning,
dialogue systems, amongst others. However, present-day models are not trained
to produce affective content that can emulate human communication. Such
abilities are desirable in many applications such as comment/review generation
(Dong et al., 2017), and emotional chatbots (Zhou et al., 2018; Ma et al.,
2020).

(a)

(b)

Figure 13: Our proposed conceptual framework for conditional generative
conversation modeling. A dyadic conversation between person X and Y are
governed by interactions between several latent factors, such as, intent,
emotion. (a) The complete emotion-aware conditional generative model that
accounts for the variables both internal and external to the interlocutors.
(b) A simplified version where the presence of external and sensory inputs are
ignored.

Early efforts in this direction included works that either focused on related
topics such as personality-conditioned text generation (Mairesse & Walker,
2007) or pattern-based approaches for the generation of emotional sentences
(Keshtkar & Inkpen, 2011). These works were significantly pipe-lined with
specific modules for sentence structure and content planning, followed by
surface realization. Such sequential modules allowed constraints to be defined
based on personality/emotional traits, which were mapped to sentential
parameters that include sentence length, vocabulary usage, or part-of-speech
(POS) dependencies. Needless to say, such efforts, though well-defined, are
not scalable to general scenarios and cross-domain settings.

####  3.8.1 Conditional Generative Models

We, human beings, count on several variables such as emotion, sentiment, prior
assumptions, intent, or personality to participate in dialogues and
monologues. In other words, these variables control the language that we
generate. Hence, it is outrageous to claim that a vanilla seq2seq framework
can generate _near perfect_ natural language. Recently, conditional generative
models have been developed to address this task. Conditioning on attributes,
such as, sentiment can be approached in several ways. One way is by learning
disentangled representations, where the key idea is to separate the textual
content from high-level attributes, such as, sentiment and tense in the hidden
latent code. Present approaches utilize generative models such as VAEs (Hu et
al., 2017), GANs (Wang & Wan, 2018) or Seq2Seq models (Radford et al., 2017).
Learning disentangled representations is presently an open area of research.
Enforcing independence of factors in the latent representation and presenting
quantitative metrics to evaluate the factored hidden code are some of the
challenges associated with these models.

An alternate method is to pose the problem as an attribute-to-text translation
task (Dong et al., 2017; Zang & Wan, 2017). In this setup, desired attributes
are encoded into hidden states which condition upon a decoder tasked to
generate the desired text. The attributes could include user’s preferences
(including historical text), descriptive phrases (e.g. product description for
reviews), and sentiment. Similar to general translation tasks, this approach
demands parallel data and raises generalization challenges, such as, cross-
domain generalization. Moreover, the attributes might not be available in the
desired formats. As mentioned, attributes might be embedded in conversational
histories, which would require sophisticated NLU capabilities similar to the
ones used in task-oriented dialogue bots. They might also be in the form of
structured data, such as Wikipedia tables or knowledge graphs, tasked to be
translated into textual descriptions, i.e., data-to-text – an open area of
research (Mishra et al., 2019a).

##### Our conceptual conditional generative model

In Fig. 13, we illustrate a dialogue-generation mechanism that leverages these
key variables. In this illustration, $P$ represents the personality of the
speaker; $S$ represents the speaker-state; $I$ denotes the intent of the
speaker; $E$ refers to the speaker’s emotional-aware state, and $U$ refers to
the observed utterances. The definitions of these variables are as given
below:

Topic: Topic is a key element that governs and drives a conversation. Without
knowing the topical information, dialogue understanding can be incomplete and
vague.

Personality ($P$): As per the standard definition of personality, this
variable controls the basic behavior of a person under varied circumstances.
Personality can also signify different dimensions, such as, values, needs,
goals, agency, and more, according to the theory of appraisals (Ellsworth &
Scherer, 2003) in affective computing.

Observed conversational history ($U$): The observed utterances in the
conversational history are represented as $U$. $U$ provides contextual
information and play a critical role in constructing other states such as $S$
and $I$.

Background knowledge ($B$): Background knowledge represents the prior
assumptions, pre-existing inter-speaker relations, speaker’s knowledge and
opinion about the topic and any other background or external information that
are not explicitly present in the conversational history. Such knowledge
usually evolves over time depending on how the speaker experiences the
environment and interacts with it.

Speaker-state ($S$): Speaker-state can be defined as the latent memory of a
speaker that evolves over the turns in the conversation. This memory contains
the information obtained through the process of cognition and thinking. For
example, let us consider this conversation:

A (excited and happy): You know I am getting married!
B (excited and happy): Wow! that’s great news. Who is that lucky person? When
is the ceremony?

In this conversation, person B listens to person A (refer to variable $U$ in
Fig. 13) and applies cognition and thinking to construct a latent memory which
we call as $S$. $S$ can also rely on the background knowledge $B$.

Intent ($I$): Intent defines the goal that the speaker wants to achieve in the
conversation. Intent can be triggered after cognition and thinking i.e., $S$.

In the above example, it is obvious that the intent of person B (i.e.,
intention to know who is person A marrying and the date of the ceremony) is
governed by his/her cognition after hearing the statement by person A. Intent
can heavily rely on the personality of the speaker.

External and sensory inputs ($Q$): In the process of a conversation, certain
sensory or other external events can directly initiate cognition and affect.
We call these inputs as $Q$. These inputs can often be non-verbal cues.
Affective reactions to these sensory inputs can occur with or without any
complex cognitive modeling. When the stimulus is sudden and unexpected, the
affective reaction can occur before evaluating and appraising the situation
through cognitive modeling. This is called _Affective Primacy_ (Zajonc, 1980).
For example, our immediate reaction when we encounter an unknown creature in
the jungle without evaluating whether it is safe or dangerous. Sensory inputs
$Q$ can also trigger cognitive modeling for subsequent evaluation of the
situation and thus can update the latent speaker-state $S$.

Emotional-aware state ($E$): Emotional-aware state encodes the emotion of the
speaker at time $t$. As proposed by the psychology theorist Lazarus in his
article (Lazarus, 1982), the emotional-state can be triggered by cognition and
thinking, we think in a conversation, this state should be controlled by $S$
and $I$. If we refer to the same example above, the expressed emotion by
person B depends on the speaker-state $S$ and intent $I$. According to the
theory of affective primacy by (Zajonc, 1980), the affective or emotional
state does not always depend on cognition, and in various situations, an
affective response can be spontaneous without relying on any prior cognitive
evaluation of the situation e.g., we fear when we encounter a snake, our eyes
blink when we are exposed to sudden bright light source. Based on this theory,
the state $E$ may not always depend on $S$ and $I$. In the case of affective
primacy, affect directly depends on the sensory or external inputs, i.e., $Q$
which are very important in multimodal conversations.

The workflow: At turn $t$, the speaker conceives several pragmatic concepts,
such as argumentation logic, viewpoint, personality, conversational history,
emotion coding sequence of the interlocutors in the conversation and inter-
personal relationships—which can collectively construct the latent speaker-
state $S$ (Hovy, 1987) by the means of cognition. Next, the intent $I$ of the
speaker is formulated based on the current speaker-state, personality and
previous intent of the same speaker (at $t-2$). Personality $P$, speaker-state
$S$, intent $I$ and external or sensory inputs $Q$ can jointly influence the
emotion $E$ of the speaker. Finally, the intent, the speaker state, and the
speaker’s emotion jointly manifest as the spoken utterance.

##### Aspect-level text summarization

One application of conditional generative models is aspect-level text
summarization( Fig. 14). Aspect-level text summarization task consists of two
stages: aspect extraction and aspect summarization. The first stage distills
all the aspects discussed in the input text. The latter generates an
abstractive or extractive gist of the extracted aspects from the source text.
In the case of abstractive gist, the model generates content conditioned on
the given aspect. Existing works (Frermann & Klementiev, 2019) address this
task by leveraging aspect-level document segmentation and use that to generate
both abstractive and extractive aspect-level summaries.

Figure 14: Aspect-level text summarization: an application of conditional
generative modeling.

####  3.8.2 Sentiment-Aware Dialogue Generation

The area of affect-controlled text has also percolated into dialogue systems.
The aim here is to equip emotional intelligence into these systems to improve
user interest and engagement (Partala & Surakka, 2004; Prendinger & Ishizuka,
2005). Two key functionalities are important to achieve this goal (Hasegawa et
al., 2013):

  1. 1.

Given a user query, determine the best emotional/sentiment response adhering
to social rules of conversations.

  2. 2.

Generate the response eliciting that emotion/sentiment.

Present works in this field either approach these two sub-problems
independently (Ghosh et al., 2017) or in a joint manner (Gu et al., 2019). The
proposed models range over various approaches, which include affective
language models (Ghosh et al., 2017) or seq2seq models customized to generate
emotionally-conditioned text (Zhou et al., 2018; Asghar et al., 2018). Kong et
al. (2019) take an adversarial approach to generate sentiment-aware responses
in the dialogue setup conditioned on sentiment labels. For a brief review of
some of the recent works in this area, available corpora and evaluation
metrics, please refer to Pamungkas (2019).

Despite the recent surge of interest in this application, there remains
significant work to be done to achieve robust emotional dialogue models. Upon
trying various emotional response generation models such as ECM (Zhou et al.,
2018), we surmise, these models lack the ability of conversational emotion
recognition and tend to generate generic, emotionally incoherent responses.
Better emotion modeling is required to improve contextual emotional
understanding (Hazarika et al., 2018b), followed by emotional anticipation
strategies for the response generation. These strategies could be optimized to
steer the conversation towards a particular emotion (Lubis et al., 2018) or be
flexible by proposing appropriate emotional categories. The quest for better
text with diversity and coherence and fine-grained control over emotional
intensity are still open problems for the generation stage. Also, automatic
evaluation is a notorious problem that has plagued all applications of
dialogue models.

To this end, following the work by Hovy (1987), we illustrate a sentiment and
emotion-aware dialogue generation framework in Figure 13 that can be
considered as the basis of future research. The model incorporates several
cognitive variables, i.e., intent, sentiment, and interlocutor’s latent state
for coherent dialogue generation.

####  3.8.3 Sentiment-Aware Style Transfer

Style transfer of sentiment is a new area of research. It focuses on flipping
the sentiment of sentences by deleting or inserting new sentiment-bearing
words. E.g., to change the sentiment of _”The chicken was delicious”_ , we
need to find a replacement of the word _delicious_ that carries negative
sentiment.

Recent methods on sentiment-aware style transfer attempt to disentangle
sentiment bearing contents from other non-sentiment bearing parts in the text
by relying on rule-based (Li et al., 2018) and adversarial learning-based
(John et al., 2019) techniques.

Adversarial learning-based methods to sentiment style transfer suffer from the
lack of available parallel corpora, which opens the door to a potential future
work. Some initial works, such as (Shen et al., 2017), address non-parallel
style transfer, albeit with strict model assumptions. We also think this
research area should be studied together with the ABSA (aspect-based sentiment
analysis) research to learn the association between topics/aspects and
sentiment words. Considering the example above, learning better association
between topics/aspects and opinionated words should aid a system to substitute
_delicious_ with _unpalatable_ instead of another negative word _rude_.

###  3.9 Bias in Sentiment Analysis Systems

Fairness in machine learning has gained much traction recently. Amongst
multiple proposed definitions of fairness, we particularly look into ones that
attempt to avoid disparate treatment and thus reduce the disparate impact
across demographics. This requires removing bias that favors a subset of the
population with an unfair advantage.

Studying fairness in sentiment analysis is crucial, as diverse demographics
often share the derived commercial systems. Sentiment analysis systems are
often used in sensitive areas, such as healthcare, which deals with sensitive
topics like counseling. Customer calls and marketing leads, from various
backgrounds, are often screened for sentiment cues, and the acquired analytics
drives major decision-making. Thus, understanding the presence of harmful bias
is critical. Unfortunately, the field is at its nascent stage and has received
minimal attention. However, some developments have been observed in this area,
which opens up numerous research directions.

While there can be different demographics, such as gender, race, age, etc.,
our subsequent discussions, without any loss of generality, primarily
exemplify gender biases.

####  3.9.1 Identifying Causes of Bias in Sentiment Analysis Systems

Bias can be introduced into the sentiment analysis models through three main
sources:

  1. 1.

Bias in word embeddings: Word embeddings are often trained on publicly
available sources of text, such as Wikipedia. However, a survey by Collier &
Bear (2012) found that less than 15% of contributions to Wikipedia come from
women. Therefore, the resultant word embeddings would naturally under-
represent women’s point of view.

  2. 2.

Bias in the model architecture: Sentiment-analysis systems often use meta
information, such as gender identifiers and indicators of demographics that
include age, race, nationality, and geographical cues. Twitter sentiment
analysis is one such application where conditioning on these variables is
prevalent (Mitchell et al., 2013; Vosoughi et al., 2015; Volkova et al.,
2013). Though helpful, such design choices can often lead to various forms of
bias such as gender bias, geographic (location) bias, etc. from theses
conditioned variables. These model architectures can further amplify the bias
observed in the training data.

Sentiment intensity of a word or phrase can be interpreted differently across
geographical demographics. For example, the general perception about _good
weather_ , _good traffic_ , _cheap phone_ can vary among the Indian and
American populations. Hence, training a sentiment model on data originating
from one geographic location can expose the model to demographic bias when
applied to data from other locations. Thus, depending on the end application
and region of interest, a cogent solution to this issue could be to develop
demographic-specific sentiment analysis models rather than creating a generic
one. However, as data is often a combination of multiple demographic classes —
e.g. a combination of various ages, genders, and locations, etc. — building
mutually exclusive demographic-based models would be computationally
prohibitive. Nevertheless, research in domain adaptation is one possible
avenue towards solving this problem as it would help adapting a trained
sentiment model on a source demographic data to learn the features of the
target demography.

  3. 3.

Bias in the training data: There are different scenarios where a sentiment-
analysis system can inherit bias from its training data. These include highly
frequent co-occurrence of a sentiment phrase with a particular gender — for
example, _woman_ co-occurring with _nasty_ —, over- or under-representation of
a particular gender within the training samples, strong correlation between a
particular demographic and sentiment label — for instance, samples from
_female_ subjects frequently belonging to _positive_ sentiment category.

An author’s stylistic sense of writing can also be one of the many sources of
bias in sentiment systems. E.g., one person uses strong sentiment words to
express a positive opinion but prefers to use milder sentiment words in
exhibiting negative opinions. Similarly, sentiment expression might vary
across races and genders, e.g., as shown in a recent study by Bhardwaj et al.
(2020). As a result, a sentiment analysis model might show a drastic
difference in the sentiment intensities when the gender word in the same
sentence is changed from masculine to feminine or vice versa, making the task
of identifying bias and de-biasing difficult.

####  3.9.2 Evaluating Bias

Recent works present corpora that curate examples, specifically to evaluate
the existence of bias. The Equity Evaluation Corpus (EEC) (Kiritchenko &
Mohammad, 2018) is one such example that focuses on finding gender and racial
bias. The sentences in this corpus are generated using simple templates, such
as “`<Person>` made me feel `<emotional state word>`”, “`<Person>` feels
angry”. The variable `<Person>` can be a female name such as “Jasmine”, or a
male name such as “Alan”. A pre-trained sentiment or emotion intensity
predictor is then tasked to predict the emotion and sentiment intensity of the
sentences. According to the task setting, a model can be called gender-biased
when it consistently or significantly predicts higher or lower sentiment
intensity scores for sentences carrying female-names than male-names, or vice
versa. The EEC corpus contains $7$ templates of type: `<Person>` and
`<emotional state word>`. The placeholder `<Person>` can be filled by any of
60 gender-specific names or phrases. Out of those 60 gender-specific names, 40
are gender-specific names (20-female, 20-male). Rest 20 are noun phrases
grouped as female-male pairs such as “my mother” and “my father”. Variable
`<emotional state word>` can take four emotions – Anger, Fear, Sadness, and
Joy – each having 5 representative words 888e.g.,:- {angry, enraged}
represents a common emotion, anger. Thus, there are 1200 ($60\times 5\times
4$) samples for each template. Finally, there are 8400 ($7\times 1200$)
samples equally divided in female and male-specific sentences
($60\times(5\times 4)\times 7=4200$ each) and 4 emotion categories ($5\times
7\times 60=2100$ each). We refer readers to Kiritchenko & Mohammad (2018) for
an elaborative explanation on the EEC corpus. While this is a good step, the
work is limited to exploring bias that is related only to gender and race.
Moreover, the templates utilized to create the examples might be too
simplistic, and identifying such biases and de-biasing them might be
relatively easy. Future work should design more complex cases that cover a
wider range of scenarios. Challenge appears when we have scenarios like “
_John told Monica that she lost her mental stability_ ” vs. “ _John told Peter
that he lost his mental stability_ ”. If the sentiment polarity in either of
these two sentences is predicted significantly different from the other, that
would indicate a likely gender bias issue.

####  3.9.3 De-biasing

If a model shows the sign of bias because of the training data, one approach
to curb this bias is to distill a subset that does not display any explicit
indication of bias. However, often it is unfeasible to collate data meeting
such constraints. Hence, it is the responsibility of researchers to come up
with solutions that can de-bias any bias introduced by the training data and
model architecture.

The primary approach to de-biasing is to perturb a text with word substitution
to generate counterfactual cases in the training data. These generated
instances can then be used to regularize the learning of the model, either by
constraining the embedding spaces to be invariant to the perturbations or
minimizing the difference in predictions between both the correct and
perturbed instances. While recent approaches, such as (Huang et al., 2019b),
have proposed these methods in language models, another direction could be to
mask out bias contributing terms during training. However, such a method
presents its own challenges since masking might cause semantic gaps.

In general, we observe that while many works demonstrate or discuss the
existence of bias, and also propose bias detection techniques, there is a
shortage of works that propose de-biasing approaches.

Existing studies mostly focus on identifying gender-bias in context-
independent word representations such as GloVe (Bolukbasi et al., 2016; Kaneko
& Bollegala, 2019; Kumar et al., 2020). Contrarily, BERT word-to-vector(s)
mapping is highly context-dependent which makes it difficult to analyse biases
intrinsic to BERT. While a lot has been studied, identified, and mitigated
when it comes to gender-bias in static word embeddings (Bolukbasi et al.,
2016; Zhao et al., 2018c; Caliskan et al., 2017; Zhao et al., 2018b), very few
recent works study gender-bias in contextualized settings. In Zhao et al.
(2019); Basta et al. (2019); Gonen & Goldberg (2019), the authors apply
debiasing on ELMo. Kurita et al. (2019) propose a template-based approach to
quantify bias in BERT. Sahlgren & Olsson (2019) study bias in both
contextualized and non-contextualized Swedish embeddings.

Apart from the traditional bias in models, bias can also exist at a higher
level when making research choices. A simple example is the tendency of the
community to resort to English-based corpora, primarily due to the notion of
increased popularity and wider acceptance. Such trends diminish the research
growth of marginalized topics and study of arguably more interesting languages
– a gap which widens through time (Hovy & Spruit, 2016). As highlighted in
Section 3.6, as a community, we should make conscious choices to help in the
equality of under-represented communities within NLP and Sentiment Analysis.

##  4 Conclusion

Sentiment analysis is often regarded as a simple classification task to
categorize contents into positive, negative, and neutral sentiments. In
contrast, the task of sentiment analysis is highly complex and governed by
multiple variables like human motives, intents, contextual nuances.
Disappointingly, these aspects of sentiment analysis remain either un- or
under-explored.

Through this paper, we strove to diverge from the idea that sentiment
analysis, as a field of research, has saturated. We argued against this
fallacy by highlighting several open problems spanning across subtasks under
the umbrella of sentiment analysis, such as aspect-level sentiment analysis,
sarcasm analysis, multimodal sentiment analysis, sentiment-aware dialogue
generation, and others. Our goal was to debunk, through examples, the common
misconceptions associated with sentiment analysis and shed light on several
future research directions. We hope this work would help reinvigorate
researchers and students to fall in love with this immensely interesting and
exciting field, again.

## Acknowledgements

The authors are indebted to all the pioneers and researchers who contributed
to this field.

This research is supported by A*STAR under its RIE 2020 Advanced Manufacturing
and Engineering (AME) programmatic grant, Award No. - A19E2b0098, Project name
- K-EMERGE: Knowledge Extraction, Modelling, and Explainable Reasoning for
General Expertise. Any opinions, findings, and conclusions or recommendations
expressed in this material are those of the authors and do not necessarily
reflect the views of A*STAR.

## Note

This paper will be updated periodically to keep the community abreast of any
latest developments that inaugurate new future directions in sentiment
analysis. The most updated version of this article can be found at
https://arxiv.org/pdf/2005.00357.pdf.

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