# A Review of Datasets for Aspect-based Sentiment Analysis

Siva Uday Sampreeth Chebolu1, Franck Dernoncourt2,
Nedim Lipka3, Thamar Solorio1,4
1University of Houston, 4800 Calhoun Rd, Houston, TX, 77004, USA
2Adobe Research, 801 N 34th St, Seattle, WA 98103, USA
3Adobe Research, 321 Park Ave, San Jose, CA, 95110, USA
4MBZUAI, Masdar City, Abu Dhabi, UAE
<EMAIL_ADDRESS>
<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Aspect-based sentiment analysis (ABSA) is a natural language processing
problem that analyzes user-generated reviews to determine a) the target entity
being reviewed, b) the high-level aspect to which it belongs, and c) the
sentiment expressed toward the targets and the aspects. Numerous yet scattered
corpora for ABSA make it difficult for researchers to identify corpora best
suited for a specific ABSA subtask quickly. This study presents a database of
corpora that can be used to train and evaluate autonomous ABSA systems.
Additionally, we provide an overview of the major corpora for ABSA and its
subtasks and highlight several features that researchers should consider when
selecting a corpus. Finally, we discuss the advantages and disadvantages of
existing dataset collection approaches and make recommendations for future
corpora creation. This survey examines 98 publicly available ABSA datasets
covering over 25 domains, including 77 English and 21 other languages datasets
(https://github.com/RiTUAL-UH/ABSA-Datasets-Info).

##  1 Introduction

Consumers, product makers, and service providers benefit differently from
user-generated reviews on e-commerce platforms. Reading about previous
customer experiences can assist future customers in making informed decisions.
At the same time, the characteristics that elicit user feedback may help
manufacturers and merchants develop measures to enhance customer satisfaction.
Furthermore, as the data grows daily at a rapid pace, there is a need to
recognize and extract sentiment or opinion from text reviews automatically.
Opinion mining or Sentiment Analysis Pang et al. (2002); Turney (2002) is a
technology that combines computational linguistics and natural language
processing to extract such opinions.

Aspects can be a feature, a trait, or a behavior of a product or an entity,
like the atmosphere of a restaurant, the performance of a laptop, the display
of a phone, and so on. The sentiment analysis focused on a finer degree,
namely, aspect-based sentiment analysis (ABSA) Hu and Liu (2004a), determines
the sentiment for each entity as well as its aspects Poria et al. (2020). Many
systems, metrics, and subtasks are created along with various corpora to solve
the task. The goal of ABSA is to extract four elements: 1) the target entity
of an opinion, 2) the high-level aspect the entity belongs to, 3) the actual
opinion phrase, and 4) finally give a sentiment polarity to the specific
target-aspect-opinion triple. For example, in the review “The pizza is very
tasty.”, pizza is the target entity, FOOD is the aspect category, very tasty
is the opinion phrase, and the sentiment polarity for pizza-FOOD-very tasty is
positive. The subtasks get their names from the subset of identified elements
in that study.

Although there has been significant research on ABSA in the last two decades,
it has become more popular after its formal introduction as a task in the
SemEval-2014. SemEval-2015 consolidated its subtasks into a single framework
in which all detected elements of expressed opinions (i.e., aspects, opinion
and target expressions, and sentiment polarities) comply with a set of
criteria and are related via sentence-level tuples. However, a user may be
interested in the text’s overall rating on a particular aspect. These ratings
may be used to calculate the average sentiment for each aspect based on
several sentences of a single review. Thus, in addition to sentence-level ABSA
annotations, SemEval-2016 Task 5 included text-level ABSA , showing that ABSA
can be performed at 1) Sentence-level and 2) Review-level Chebolu et al.
(2022).

Given the wide range of ABSA subtasks and techniques, researchers may find it
challenging to establish which corpora are optimal for a specific research
task. We want to solve this difficulty by providing an overview of available
corpora and evaluating their applicability for fundamental ABSA tasks.
Specifically, this research aims to review and summarize the literature on
collecting text and categorical values for ABSA elements, explain what has
been learned to date, and give recommendations for constructing future
datasets.

###  1.1 What is different from Previous Surveys?

The primary difference between this survey and previous ones on ABSA Laskari
and Sanampudi (2016); Schouten and Frasincar (2016); Suresh and Raghavi
(2016); Sethi and Bhattacharyya (2017); Sabeeh and Dewang (2018); Do et al.
(2019); Ahmet and Abdullah (2020); Nazir et al. (2020); Brauwers and Frasincar
(2021) is that the previous work primarily focusses on the tasks, conduct a
critical analysis of the techniques, and offer ideas and future directions for
enhancing the performance of the tasks and addressing unresolved issues. In
contrast, this research aims to review and summarize the literature on
collecting text reviews and categorical values for ABSA elements, explain what
has been learned to date, and give recommendations for constructing future
datasets. Consequently, this survey complements the previous and more current
ABSA surveys and critical retrospectives Poria et al. (2020) that focus on
definitions, methodology, and evaluations.

###  1.2 Contributions and Organization

We review 98 publicly available ABSA datasets in this survey that cover more
than 25 domains, with 76 English and 22 other language datasets that help
solve 12 different subtasks. The scope of this paper covers all the datasets
specific to Aspect-based Sentiment Analysis rather than general sentiment
analysis. We provide an overview of existing sub-tasks and current datasets,
followed by a live version of the tables as a website allowing community
additions. Following that, we look at what can be learned from current data
collection approaches and provide a few suggestions for future ABSA datasets.
We emphasize a few aspects including, the opinion phrase annotation and moving
towards review-level ABSA from sentence-level, in the discussion section, that
are particularly essential to the present ABSA research.

##  2 Tasks and Datasets Overview

This section will discuss the various tasks and subtasks associated with ABSA
and the different datasets that help solve one or more of its subtasks
independently or jointly.

###  2.1 Tasks Overview

ABSA comprises two sub-problems: 1) aspect extraction (for example, sushi,
pasta, and well-behaved staff) and 2) identifying the polarity toward each
aspect. Aspect extraction has two sub-tasks: a) extracting aspect
terms/targets and b) categorizing/normalizing the extracted aspect terms into
aspect categories. In turn, polarity detection has three subtasks: a) extract
the opinion-oriented expression, b) determine the polarity of each category or
each aspect word, and c) determine the joint polarity for aspect terms/targets
and aspect categories. For example, we have a positive sentiment polarity for
the aspect terms value, dumplings, sushi, and service and the respective
aspect categories Price, Food, Food, and Service for the review: “Highly
recommend this as great value for excellent dumplings, sushi, and service”.
The opinion phrases that are useful in determining the polarity are great, and
excellent. Therefore, the four main elements that we can identify from a given
data for different ABSA tasks and sub-tasks are 1) aspect terms/targets, 2)
aspect categories, 3) opinion phrases, and 4) sentiment polarity.

Certain terms in Aspect-Based Sentiment Analysis (ABSA) research bear multiple
labels, often used interchangeably across studies. Aspect terms are often
referred to as targets and opinion target expressions. Alternative usages of
aspect categories can be categories and entity-attribute pairs. Opinion
expressions are a span of words/tokens in a text that provide a sentiment
orientation towards an aspect term or target. These can be seen as opinion
phrases, opinion terms, opinion words, sentiment phrases, sentiment words, and
similar vocabulary. The term aspect can be ambiguous, as some researchers use
it to denote aspect terms, while others use it for aspect categories. The
exact meaning often hinges on the specific task being tackled and the elements
emphasized within that task.

Restaurant Review (sentence): The pasta was very yummy but the place has some
weird smell.
List of Aspect Categories in the dataset: {Food, Ambience, Service, Price,
General}  Year Paper Common Name Acr. Input Expected Output 2004 Hu and Liu
(2004a) Aspect Term Extraction1 ATE sentence pasta, place 2004 Hu and Liu
(2004a) Aspect Term Sentiment Analysis1 ATSA sentence, (pasta, place)
positive, negative 2014 Pontiki et al. (2014) Aspect Category Detection1 ACD
sentence Food, Ambience 2014 Pontiki et al. (2014) Aspect Category Sentiment
Analysis1 ACSA sentence, (Food, Ambience) positive, negative 2015 Pontiki et
al. (2015) Target Detection1 TD sentence pasta, place 2014 Pontiki et al.
(2014) Target Aspect Detection2 TAD sentence (pasta, Food), (place, Ambience)
2018 Schmitt et al. (2018) Aspect Sentiment Joint Detection2 ASD sentence
(Food, positive), (Ambience, negative) 2019 Li et al. (2018) Target Sentiment
Joint Detection2 TSD sentence (pasta, positive), (place, negative) 2019 Peng
et al. (2020) Aspect Sentiment Triplet Extraction3 ASTE sentence (pasta, very
yummy, positive), (place, weird smell, negative) 2020 Wan et al. (2020) Target
Aspect Sentiment Detection3 TASD sentence (pasta, Food, positive), (place,
Ambience, negative) 2020 Fan et al. (2019) Target Opinion Word Extraction2
TOWE sentence (pasta, very yummy), (place, weird smell) 2021 Li et al. (2021)
Aspect-Sentiment-Opinion Triplet Extraction3 ASOTE sentence (pasta, positive,
very yummy), (place, negative, weird smell) 2021 Cai et al. (2021) Aspect-
Category-Opinion-Sentiment4$\pm\pm$ ACOS sentence (pasta, Food, positive, very
yummy), (place, Ambience, negative, weird smell) 2021 Zhang et al. (2021a)
Aspect Sentiment Quad Prediction4$\pm\pm$ ASQP sentence (pasta, Food,
positive, very yummy), (place, Ambience, negative, weird smell)

Table 1: Common sub-tasks of ABSA and their relation with the identified
elements Aspect Categories, Aspect Terms (a.k.a Targets), Opinion Phrases, and
Sentiment Polarity. sentence: Review Sentence. Acr.: Acronym for the Common
Name. 1: single outcome sub-task, 2, 3, 4: joint outcome sub-tasks. $\pm\pm$:
These tasks are similar but have been defined separately by different authors.

We present an overview of all the sub-tasks that stemmed from ABSA in Table 1.
We provide the year and the paper in which the sub-task was first introduced
along with the task name (as both acronym and full common name), the inputs of
each task (as Input), and their identified subset of elements (as Expected
Output) as the columns. For example, Aspect-Category Sentiment Analysis (ACSA)
aims to identify the polarity of a given aspect category. However, the Target-
Aspect-Sentiment Detection (TASD) task jointly identifies the targets, aspect
categories, and the polarity expressed towards the target-category pair. The
last four rows in the Table are recently created tasks that include
identifying opinion phrases in the given text that align with the sentiment
polarity towards targets or aspects.

###  2.2 Task Challenges

There are a few major challenges for ABSA and its sub-tasks. Firstly, each of
the elements described above is not independent but rather depends on other
elements’ detection. For example, aspect term extraction and aspect category
detection tasks can be used in tandem to find terms and categories in a review
Wan et al. (2020); Xue et al. (2017). The aspect term extractor may extract
related aspect terms and vice versa if it knows which aspect categories a
review belongs to. In the review, “However, it’s the service that leaves a
sour taste in my mouth.” The term service is explicitly used, indicating the
aspect service. If the aspect term extractor is aware of the aspect category
Service, it gives the word service in the review greater weight. Similarly, if
the term service is given higher weight in the review, the aspect category
detector can identify the Service category easily. Also, we need to detect the
implicit phrase “sour taste in my mouth” as a sentiment indicator to know that
the review conveys a negative sentiment polarity towards service. This phrase
is an idiom with a negative connotation for service. The literal meaning
should not be considered in this situation because the criticism is not
directed toward any of the restaurant’s food or beverages. There is some
dedicated research on the implicit aspect and its sentiment detection Cruz et
al. (2014), which could be leveraged to improve the overall detection
performance.

Another issue is ABSA’s relevance and reliance on several other NLP tasks. It
is worth noting that not every entity described in a text is an aspect.
Entities that are the subject of an opinion are referred to as aspects. We
require a sophisticated NER system to identify the names of foods, beverages,
restaurants, computers, processors, hotels, and other items that may be
possible targets/aspect-terms in the provided review sentence. To address the
common opinion issue, we must find opinion phrases in the supplied review and
link them to all of the proper entities. This issue is closely connected to
the NLP Entity-Linking problem Daiber et al. (2013); Kolitsas et al. (2018).
excellent is used for both food and margaritas in the review “the food was
excellent, the margaritas too.” On the other hand, there is no explicit
reference to a target entity in this review “creative and tasty but pricey,”
thus it should be assumed that the opinion is conveyed on FOOD. However, we
must conclude that the aspect is RESTAURANT rather than FOOD in the restaurant
review “Good and affordable.” To address these issues, we must model the ABSA
problem jointly with the related sub-problems such as aspect-term
identification (NER), polarity and opinion detection (opinion phrases),
syntactic simplification Siddharthan (2006); Scarton et al. (2017) to get
separate sentences for each opinion-entity pair to solve the common-opinion
problem.

###  2.3 Datasets Overview

All the publicly available datasets for ABSA are presented in Tables 7 and 8.
We provide the paper introducing the dataset, with its citation, dataset’s
source and domain, number of reviews and the respective number of sentences,
and other statistics such as the number of sentences that are annotated with
positive, negative and neutral sentiment polarity for the aspect terms/targets
or aspect categories. In Table 2, we show which ABSA sub tasks from Table 1
could be evaluated using the datasets from Table 7 and 8.

The SemEval challenge datasets and the recently published SentiHood and MAMS
corpora are the most extensively used corpora for aspect-based sentiment
analysis. The SemEval corpora were made public as part of a shared work held
during the International Workshop on Semantic Evaluation, held annually from
2014 to 2016. The datasets are described in full in Pontiki et al. (2014,
2015, 2016). Historically, ABSA was primarily concerned with aspect term
extraction and sentiment analysis Hu and Liu (2004a). Before 2014, there has
been very little research into aspect category detection and sentiment
analysis. However, as ACD was formally presented at SemEval-2014, a slew of
new challenges arose. ABSA has witnessed positive outcomes across all tasks
thanks to the emergence of artificial neural networks.

Despite the popularity of SemEval datasets for this work, most sentences only
include one or many aspects with the same sentiment polarity, reducing the
ABSA task to sentence-level sentiment analysis. Jiang et al. (2019) published
a new large-scale Multi-Aspect Multi-Sentiment (MAMS) dataset, in which each
phrase has at least two independent aspects with different sentiment polarity.
Although Jiang et al. (2019) claimed that each sentence has more than one
aspect-sentiment tuple, the approach they followed is not realistic. When
there is only one opinion tuple in a sentence, they introduce either a
“miscellaneous” category or another category with neutral sentiment as a
second opinion tuple that does not have an opinion in the review. For
instance, in the following review, I like the smaller portion size for
dinner., there is only one opinion, which is about the food’s portion size.
However, the actual annotation has two opinion tuples: one is for the food,
and the other is a neutral opinion on the restaurant’s miscellaneous aspect
category. We do not dispute the legitimacy of this strategy, but we do not
find it practical in the real world. Another drawback with the SemEval corpora
is that they contain reviews about a single target entity, such as a laptop or
restaurant. To overcome this, Saeidi et al. (2016) created the SentiHood
dataset to identify the sentiment towards each aspect of one or more entities.

As discussed previously, an opinion phrase is critical in determining the
sentiment polarity towards an aspect or a target and, sometimes, determining
to which target/aspect that opinion belongs. Fan et al. (2019); Peng et al.
(2020) modified the SemEval datasets to account for the missing annotation of
opinion phrases that lead to a specific sentiment polarity for the target,
aspect term, or aspect category. However, this resulted in a few instances,
coercing them to merge all the reviews from SemEval 2014 to 2016 into a single
dataset.

Dataset Paper | ATE | ATSA | ACD | ACSA | TD | TSD | ASD | TAD | TASD | ASTE | TOWE | QUAD.Ex
---|---|---|---|---|---|---|---|---|---|---|---|---
Customer Reviews Hu and Liu (2004b) | ✓ | ✓ | - | - | - | - | - | - | - | - | - | -
Customer Reviews Ding et al. (2008) | ✓ | ✓ | - | - | - | - | - | - | - | - | - | -
JDPA Kessler et al. (2010) | - | - | - | - | ✓ | ✓ | - | - | - | - | - | -
Darmstadt Service Toprak et al. (2010) | - | - | - | - | ✓ | ✓ | - | - | - | - | - | -
TripAdvisor Hotels Wang et al. (2011) | - | - | ✓ | ✓ | - | - | ✓ | - | - | - | - | -
Czech Restaurants Steinberger et al. (2014) | ✓ | ✓ | ✓ | ✓ | - | - | - | - | - | - | - | -
SE-14 Restaurants Pontiki et al. (2014) | ✓ | ✓ | ✓ | ✓ | - | - | - | - | - | - | - | -
SE-14 Laptops Pontiki et al. (2014) | ✓ | ✓ | - | - | - | - | - | - | - | - | - | -
Twitter Comments Dong et al. (2014) | - | - | - | - | ✓ | ✓ | - | - | - | - | - | -
Customer Reviews Liu et al. (2015) | ✓ | ✓ | - | - | - | - | - | - | - | - | - | -
HAAD Al-Smadi et al. (2015) | - | - | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | - | - | -
SE-15 Restaurants Pontiki et al. (2015) | - | - | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | - | - | -
SE-15 Laptops Pontiki et al. (2015) | - | - | ✓ | ✓ | - | - | ✓ | - | - | - | - | -
SE-16 Rest & Hotels Pontiki et al. (2016) | - | - | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | - | - | -
SE-16 Telecom Pontiki et al. (2016) | - | - | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | - | - | -
SE-16 Laptops Pontiki et al. (2016) | - | - | ✓ | ✓ | - | - | ✓ | - | - | - | - | -
SE-16 Mob.Phns.Pontiki et al. (2016) | - | - | ✓ | ✓ | - | - | ✓ | - | - | - | - | -
SE-16 Dig.Cam. Pontiki et al. (2016) | - | - | ✓ | ✓ | - | - | ✓ | - | - | - | - | -
SentiHood Saeidi et al. (2016) | - | - | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | - | - | -
GermEval-2017 Wojatzki et al. (2017) | - | - | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | - | - | -
BeerAdvocate, TripAdvisor Yin et al. (2017) | - | - | ✓ | ✓ | - | - | ✓ | - | - | - | - | -
ABSITA-2018 Basile et al. (2018) | - | - | ✓ | ✓ | - | - | ✓ | - | - | - | - | -
FiQA de França Costa and da Silva (2018) | - | - | ✓ | ✓ | ✓ | ✓ | ✓ | - | ✓ | - | - | -
Ba-Re-Cr Rahman and Kumar Dey (2018) | - | - | ✓ | ✓ | - | - | ✓ | - | - | - | - | -
MAMS Jiang et al. (2019) | ✓ | ✓ | ✓ | ✓ | - | - | ✓ | - | - | - | - | -
TOWE Fan et al. (2019) | - | - | - | - | ✓ | - | - | - | - | - | ✓ | -
Telugu Movies Regatte et al. (2020) | ✓ | ✓ | ✓ | ✓ | - | - | ✓ | ✓ | - | - | - | -
ASTE Peng et al. (2020) | - | - | - | - | ✓ | ✓ | - | - | - | ✓ | ✓ | -
ASOTE Li et al. (2021) | - | - | - | - | ✓ | ✓ | - | - | - | ✓ | ✓ | -
ABSITA-2020 De Mattei et al. (2020) | ✓ | ✓ | - | - | - | - | - | - | - | - | - | -
NewsMTSC Hamborg and Donnay (2021) | ✓ | ✓ | - | - | - | - | - | - | - | - | - | -
ASAP Bu et al. (2021) | - | - | ✓ | ✓ | - | - | ✓ | - | - | - | - | -
ASQP Zhang et al. (2021a) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
ACOS Cai et al. (2021) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
DM-ASTE Xu et al. (2023) | - | - | - | - | ✓ | ✓ | - | - | - | ✓ | ✓ | -
DE-ASTE Chia et al. (2023) | - | - | - | - | ✓ | ✓ | - | - | - | ✓ | ✓ | -
MEMD-ABSA Xu et al. (2023) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓

Table 2: Related subtasks of ABSA for each Dataset. QUAD.Ex: Quadruple
Extraction. Ba-Re-Cr: Bangla Restaurants and Cricket Dataset. Note: The
acronyms of the subtasks in the column names are according to the Acr. column
in Table 1.

###  2.4 Annotation Procedure and Dataset Source

Even though researchers use various annotation methods when building ABSA
datasets, we explain the most frequent method here. One annotator (A)
initially annotates a portion of the data, which is then checked by another
annotator (B) for any corrections. The remainder of the sentences in the
dataset will be annotated by annotator A, with additional instructions based
on the nature of the earlier disagreements. When A lacked assurance, a
decision was taken in collaboration with B. When A and B differed, they and a
third expert annotator came to a judgment together. Another conflict
resolution method was to take the vote of the majority and consider that as
the correct annotation. Since most of the datasets follow this procedure where
one annotator annotates and the expert annotator checks for the mistakes, many
of the dataset papers lack the inter-annotator agreement scores.

The SemEval-2014 (SE-14) dataset was annotated in two stages. The first stage
consisted of tagging and detecting the polarity of all single and multi-word
words that designated certain aspects of the target item. The second step
involves identifying the aspect categories and polarity of the sentences. Most
datasets that include annotations simply for aspect terms/targets and their
polarity, such as the Customer Review datasets Hu and Liu (2004b); Ding et al.
(2008); Liu et al. (2015), TOWE Fan et al. (2019), ASTE Peng et al. (2020),
follow the first stage of this process. The second stage is only implemented
for datasets including aspect categories, such as the SemEval and FiQA
datasets.

Few datasets, such as MAMS, give distinct annotations for ATSA and ACSA tasks
where there is no one-to-one correspondence between aspect words, aspect
categories, and their polarities. The restaurant datasets from SemEval (Table
7 and 8) are a subset of the datasets published by Ganu et al. (2009) that had
only six aspect categories.

A typical approach followed by researchers is to take existing datasets and
annotating them for missing items for an existing subtask or propose a new
subtask for ABSA from the annotations. The TOWE and ASTE datasets (Table 7)
are derived from SemEval restaurants and laptops. The authors included the
opinion phrase information for the existing opinion tuples to propose a new
subtask. The most common disagreements were noticed when annotating the
multiword aspect term boundaries, aspect term vs. reference to target entity,
neutral polarity ambiguity, and the problem of distinguishing aspect terms
when they appear in conjunctions or disjunctions. The last one was resolved
using the maximal phrase as the aspect term. Most of the English restaurant
datasets, such as SemEval, MAMS, TOWE, and ASTE, are obtained from
citysearch.com for New York restaurants. while the Laptop data were derived
from laptop reviews on Amazon.com. Since aspect category detection task is
formmaly introduced in SemEval-2014, all preceding datasets only include
annotations for aspect words and their polarity.

##  3 Discussion and Future Directions

We explore several characteristics of the corpora in this section, including
the formats, the need for joint datasets with opinion phrase annotations, and
recommendations for future ABSA datasets.

###  3.1 Dataset Formats

The definition and format of ABSA components vary greatly depending on the
dataset’s source. SemEval-2014, for example, published a dataset with explicit
and independent aspect categories, aspect terms, and corresponding sentiment
polarity. Because there is no one-to-one correspondence between the terms and
the categories, using aspect terms and categories in a joint detection
scenario is problematic. Therefore, one is forced to work on either the ATE
and ASTE or the ACD and ACSA. However, in SemEval-2015 and SemEval-2016, the
dataset structure is more unambiguous, establishing a link between the targets
and the aspect categories. The sentiment polarity is linked to the target-
aspect category combination. It allows the community to recognize a text’s
stated sentiment better using terms and categories. Again, in SemEval-2015 and
SemEval-2016, the aspect category is divided into 1) Entity and 2) Attribute.
Entities can be the reviewed entity itself, such as the RESTAURANT, a
part/component of it, such as AMBIENCE, or another relevant entity, such as
DRINKS. Attributes are facets of an entity such as PRICE or QUALITY.

Jiang et al. (2019), Regatte et al. (2020) and a few others followed the
SemEval-2014’s XML format and released new datasets in the recent past.
However, Fan et al. (2019); Peng et al. (2020) modified the datasets from all
three SemEval shared tasks into another format to include the opinion phrase
and released the datasets in an XML and NER task’s BIO (beginning, inside, and
outside) format.

On the other hand, the SentiHood dataset used a JSON format to provide the
annotations for targets, aspects, and sentiments. But the definition of aspect
category in the SentiHood dataset is the combination of the target and the
aspect, leading to identifying ACD and ACSA tasks from Table 1. As mentioned
in Section 2.3, aspect categories are formally introduced in SemeEval-2014.
All the prior datasets only annotate the aspect terms and their sentiment
polarity to solve the ATE and the ATSA tasks. Therefore, for more robust ABSA
systems, we urge that the community use an already established structure and
criteria for future datasets rather than introducing a new format or
structure. The standardization of the annotation format would also benefit
benchmarking and updating existing models without any adjustments to the
architecture.

###  3.2 Opinion Phrases and Datasets Merging

As previously explained in Section 2.2, we must identify the opinion words in
a given text to determine the sentiment polarity and the entities on which the
opinion is conveyed, i.e., the aspect terms. It is evident from Table 2 that
most recent tasks, such as the ASTE, TOWE, and ASQP, annotated the opinion
words in the current SemEval shared task datasets to enhance the ABSA task.

In the original datasets of the SemEval challenge, the opinion targets (aspect
terms) are annotated, but the opinion words and their correspondence with
targets are not provided. In addition, most of the available benchmark corpora
are small. This gives the opportunity to combine or merge datasets with
similar characteristics. For instance, Fan et al. (2019) annotated the
corresponding opinion words for the annotated targets. The sentences without
targets or with implicit opinion expressions are not included. The original
ASTE dataset does not contain cases where a single opinion span is associated
with multiple targets. Consequently, Peng et al. (2020) refined the dataset
with these additional missing triplets and expanded the corpora.

The community could focus on merging the existing datasets to obtain better
quality corpora with increased sizes. Furthermore, researchers could annotate
for the opinion words missing in most of the current datasets, which could
greatly improve the overall performance of ABSA.

Review: I picked the asparagus, which was incredible. It was steamed and
tossed with garlic. The steak was nice and juicy. It’s served with either a
peppercorn sauce or red wine reduction. The service from the staff was
extremely attentive and very friendly. It was the highlight of our dinner.
Target Aspect Cat. Opinion Expr Sent. asparagus FOOD#QLTY incredible Positive
NULL FOOD#STY_OP steamed and tossed with garlic Positive steak FOOD#QLTY nice
and juicy Positive NULL FOOD#STY_OP served with either a peppercorn sauce or
red wine reduction Positive service from the staff SERVICE#GEN extremely
attentive and very friendly Positive NULL FOOD#QLTY (OR) AMBIANCE#GEN
highlight Positive

Table 3: Result of applying Sentence-Level methods to full-review to detect
Target, Aspect, Opinion, and Sentiment.

###  3.3 Need Large Datasets for Unified Models

Recent ABSA datasets are mostly drawn from SemEval shared challenges and
include additional data processing and task-specific annotations. The small
number of instances (for example, hundreds of phrases) in each dataset makes
it challenging to compare models with reliability, particularly Transformer-
based models with millions of parameters. Researchers currently evaluate a
model’s accuracy by averaging the results of numerous runs, but larger
datasets would allow for more precise comparisons. However, more challenging
datasets must still be provided to meet real-world scenarios that include
reviews from many domains such as Xu et al. (2023); Chia et al. (2023) or
languages, for example, can help evaluate multi-domain and multi-lingual ABSA
systems.

Recently, the unified models built using the generative frameworks Chebolu et
al. (2021); Zhang et al. (2021b) yield SOTA performance on all the subtasks of
ABSA by jointly solving for all the elements. The advantage of these unified
models is that they could solve multiple subtasks without a change in the
model architecture. Building more datasets similar to Zhang et al. (2021a);
Cai et al. (2023), with annotations for all the elements, would be beneficial
in developing and investigating these promising types of models.

###  3.4 Datasets in Low-Resource Languages

It is evident from the presented tables and preceding discussion that the
majority of ABSA’s available datasets are in English. Very few datasets are
available for low-resource languages, where in most cases, just one dataset is
accessible. Numerous studies employed cross-lingual approaches to
automatically produce new datasets for low-resource languages, or to tackle
the target language using data from the source language, or for other
purposes. A few works first translated from the source to the target language
with an off-the-shelf translation system and then aligned the labels using
FastAlign or some other softwares Barnes et al. (2016); Klinger and Cimiano
(2015). Others use the cross-lingual word embeddings pre-trained on large
parallel bilingual corpus, or utilize the multilingual Pre-trained Language
Models (mPLMs) such as multilingual BERT Devlin et al. (2019) and XLM-RoBERTa
Conneau et al. (2020), or the zero-shot learning Jebbara and Cimiano (2019).
Despite these efforts, the quality of the datasets for low-resource languages
are still under par. It could also be attributed to the fact that the XABSA
problem is relatively under-explored compared to the monolingual ABSA.
Although mPLMs are widely used for various cross-lingual NLP tasks nowadays,
exploring their usage in the XABSA can be tricky since language-specific
knowledge plays an essential role in any ABSA task Zhang et al. (2022).

It explains the need to annotate datasets in different languages to push the
boundaries of ABSA research ahead. Given the fact that sentiment analysis is a
very subjective task, if researchers do not compromise on getting high inter-
annotator agreement scores for annotating different elements of ABSA, the
quality of the datasets could be ensured with high-confidence. As an initial
step, taking the annotation costs into consideration, researchers can build
small but high quality datasets in the low-resource languages.

###  3.5 Sentence-Level to Review-Level ABSA

In real-life scenarios, reviews often contain multiple sentences with
overlapping contexts (Figure 1 in Appendix), making sentence-level ABSA
methods less effective. These methods assign a NULL value for implicit targets
and opinions within a sentence context and struggle to generalize to full
reviews. In contrast, by considering the entire review, explicit targets in
one sentence might be referred to implicitly in others, providing a richer and
more nuanced analysis. The previous focus on sentence-level ABSA limits the
applicability of these methods to real-world situations. Even tasks that
included a Text-Level ABSA component, like SE16-ABSA Pontiki et al. (2016);
Chebolu et al. (2022), were aimed at summarizing opinions from individual
sentences rather than capturing ABSA elements within the entire review
context.

Moreover, applying sentence-level ABSA to full reviews often fails to
accurately capture the intended targets of pronouns or implicit references
(Table 3). For instance, the target of a pronoun like "it" will be marked as
NULL (implicit) when sentences are considered in isolation. However, in the
broader context of the review, "it" might refer to a specific entity mentioned
in previous sentences. This lack of context makes it difficult for models to
correctly assign opinions to targets.

Implicit targets could be effectively handled by considering the full context
of a review. Likewise, the identification of correct categories also benefits
from full context analysis (Table 6 in appendix). While previous benchmark
datasets from SemEval competitions annotate opinions in a sentence based on
review context, this approach is not guaranteed to generalize to all implicit
cases, potentially leading to inconsistencies and reduced performance in
system predictions. Hence, the adoption of review-level ABSA and the creation
of corresponding datasets can significantly improve the effectiveness of
sentiment analysis.

###  3.6 Inter-Annotator Agreement

The inter-annotator agreement, a key metric for evaluating the quality of a
dataset, can be quantified in various ways such as the renowned Cohen’s Kappa
and Fleiss’s Kappa scores, which are used for classification tasks. However,
according to a study Hripcsak and Rothschild (2005), Kappa may not be the best
fit for span-extraction annotation in textual data. The limitation arises from
the requirement of Kappa to compute the number of negative cases, which is
unidentifiable for spans as they constitute sequences of words without a
predetermined quantity of items for annotation in a text.

Due to these limitations of Kappa metric, the F-measure, which doesn’t
necessitate the calculation of negative cases, is often more suitable for
gauging inter-annotator agreement in span extraction annotation tasks such as
target and opinion phrase extraction Deléger et al. (2012). In particular, for
datasets that encompass both classification (category and polarity) and span
extraction tasks, such as the ASQP dataset, the F-measure can effectively
serve as the chief method for inter-annotator agreement computation.

###  3.7 Is ABSA only for reviews?

A significant limitation in the current landscape of ABSA datasets is the
predominant focus on reviews and specific domains like restaurants and
e-commerce platforms. This focus on customer reviews has been fostered by the
abundance and accessibility of data in these areas. For instance, review
websites and e-commerce platforms readily provide vast amounts of customer
feedback data. While these applications have been successful, the over-
reliance on specific data types curtails the broader applicability of ABSA and
its potential to provide diverse insights across various sectors.

Diverse ABSA datasets are vital for advancing research and applications beyond
the confines of reviews. By expanding datasets to encompass domains such as
healthcare, education, finance, legal, and social issues, ABSA models can be
trained to tackle real-world challenges and address broader problem areas. For
instance, healthcare datasets could facilitate sentiment analysis of patient
feedback, improving healthcare service quality. ABSA can help identify
investor sentiment towards different aspects of a company or its financial
performance, potentially predicting stock market trends Sinha et al. (2022);
de França Costa and da Silva (2018); Ong et al. (2023); Hridoy et al. (2021).
Education datasets could uncover student sentiments towards specific aspects
of the learning environment, leading to targeted improvements Alassaf and
Qamar (2020).

Expanding datasets to these areas would enable the development of more robust
and generalizable models while enhancing decision-making processes, public
opinion analysis, and customer experiences in diverse industries.

###  3.8 Why evaluate only on a few datasets?

While ABSA research has numerous datasets at its disposal, the focus often
falls on a select few benchmark datasets. This practice, while providing
consistency and quality control, might unintentionally narrow model
adaptability and heighten biases. Benchmark datasets may not cover the breadth
of linguistic diversity in real-world scenarios, potentially causing models to
falter with different or new data. Over-reliance on these resources can
instigate model bias as models may echo the limitations of their training
data, compromising performance in varied contexts.

Addressing these issues necessitates the use of an array of datasets for
evaluation and the creation of a central platform for evaluating all ABSA
models across available datasets. By offering researchers a unified platform
for accessing diverse datasets and generating standardized metrics, we can
advance understanding of model performance. This crucial step in ABSA research
can stimulate more adaptive and resilient model development. The proposed
platform would not only assist in overcoming the limitations of benchmark
datasets but also inspire the creation of new, superior ones. While similar
initiatives have been seen in the broader NLP community Aguilar et al. (2020),
ABSA-specific platforms are still a necessity. This progressive move calls for
the combined effort of researchers and professionals alike to pave the way for
more effective and fair sentiment analysis tools.

###  3.9 Baseline Study: ABSA Task Variations Across Datasets

One of the main questions that comes to mind is why we need new datasets for
the same task(s). Let us look at a sample of datasets from Table 2 and analyze
the performance of six different ABSA subtasks from Table 1 using the unified
generative paraphrasing framework Zhang et al. (2021a). The main objective of
this experiment is to assess and contrast the intricacy of various datasets
concerning multiple tasks. Additionally, we endeavor to illustrate the inter-
dependencies among these tasks, emphasizing the imperative nature of
addressing these dependencies to enhance task performance on a given dataset.

We chose three types of joint tasks for our experiments: (a) tuple extraction
(ASD, TSD, AOPE), (b) triplet extraction (ASTE, TASD), and (c) quadruple
extraction (ASQP). The emphasis was placed on tasks involving joint extraction
rather than single-element extraction. The rationale behind this decision
stems from findings in Chebolu et al. (2021), where the authors have shown
that joint models, designed to handle multiple interrelated elements
simultaneously, consistently outperformed models that were specifically fine-
tuned for extracting a single element. The results of these experiments can be
found in Table 4.

Dataset | ASD | TSD | AOPE | ASTE | TASD | ASQP
---|---|---|---|---|---|---
ACOS_Rest | 77.03 | 75.01 | 71.25 | 68.77 | 71.71 | 65.71
ACOS_Lap | 53.15 | 74.05 | 75.21 | 74.43 | 48.21 | 57.30
DM_ASTE_Beauty | - | 65.05 | 42.94 | 45.26 | - | -
DM_ASTE_Electronics | - | 65.18 | 43.13 | 44.76 | - | -
DM_ASTE_Home | - | 67.85 | 43.93 | 44.44 | - | -
DM_ASTE_Fashion | - | 66.27 | 42.70 | 44.62 | - | -
SE-15 | 71.10 | 69.31 | 57.38 | 62.56 | 63.06 | 46.93
SE-16 | 76.97 | 75.60 | 65.80 | 71.70 | 71.97 | 57.93

Table 4: Baseline experimental results for six tasks from Table 1 using
Paraphrase-T5 method from Zhang et al. (2021a). - indicates the dataset
doesn’t have relevant data for that experiment.

Two combinations in these six tasks essentially affect ASQP positively or
negatively: 1) ASD + TSD + AOPE and 2) ASTE + TASD. For the ACOS_Rest dataset,
the performance of the ASTE and TASD task together helped the ASQP task to
detect the quadruples decently. On the other hand, in the ACOS_Lap dataset,
TSD, AOPE, and ASTE have significantly better performance when compared to ASD
and TASD. This shows that identifying aspect categories in the laptop dataset
is difficult compared to targets and opinion phrases, resulting in the low
performance of the ASQP task. On a similar note, the DM_ASTE datasets have
consistent performance across all the domains. However, the performance of the
AOPE task is far from the TSD task, indicating that it is challenging to
comprehend the complex target and opinion phrase relationship compared to the
target sentiment relationship. It negatively affected the ASTE performance, a
combination of TSD+AOPE tasks.

Datasets with higher performance in lower-level tasks don’t always guarantee
high performance in comprehensive tasks like ASQP. For instance, in SE-16,
despite ASTE and TASD being reasonably high, ASQP was lower, pointing to
complexities in merging the triplets. Given the presence of opinion phrases in
AOPE, ASTE, and ASQP, the clarity and diversity of opinion expressions in a
dataset can be pivotal. If AOPE scores are significantly lower than TASD or
ASD, it can explain why ASQP might suffer. This is clearly evident in the
se-15 and se-16 datasets. Furthermore, ASD and TSD scores in rest_acos show
the dataset has clear sentiment expressions for categories and targets. This
clarity in individual extraction didn’t fully translate to ASTE and ASQP,
suggesting that extracting opinion words/phrases concurrently with targets and
aspects might be challenging.

In conclusion, it is evident that each of these datasets presents distinct
challenges, ranging from identifying opinion terms in the SE-15 dataset to
focusing on aspect categories in the ACOS_lap dataset. Further research is
required to fully harness the inter-dependencies that exist among these tasks
utilizing the datasets. Additionally, it is crucial to investigate whether the
sub-optimal performance observed is attributed to the intrinsic complexity of
the task, the inherent ambiguity of the elements, or the inadequacies in their
representation.

##  4 Conclusion

In this survey, we highlighted the urgent need for standardization and
diversity in ABSA datasets to ensure comparability and enhance model
robustness. Emphasizing the value of opinion phrases and the potential
benefits of merging similar datasets, we advocated for the creation of
resources in low-resource languages and shifting focus to review-level ABSA.
Highlighting the importance of robust inter-annotator agreement measures, we
called for an expansion of ABSA beyond reviews and addressed the limitations
of evaluating ABSA methods on only a few datasets. Consequently, we proposed a
common platform for ABSA evaluation to foster comprehensive and fair
assessments, promoting the development of more effective sentiment analysis
tools. With these initiatives, we can advance ABSA research and ensure its
applicability across diverse linguistic and domain contexts.

##  5 Limitations

While this survey endeavored to encompass a broad array of ABSA datasets, it
is conceivable that some may have been unintentionally missed. Further, due to
space constraints, we were unable to delve deeply into datasets from other
crucial domains, including legal, healthcare, and education sectors. Even
though we performed a few experiments to understand the interplay of datasets
and tasks, we did not explore the option of combining the model predictions
from ASTE + TASD and ASD + TSD + AOPE tasks, similar to an ensemble approach,
to solve for ASQP. However, it is a non-trivial task to combine the
predictions from the triplets, as target and sentiment polarity are the only
common elements to merge, which may not be unique for an aspect category and
opinion phrase pairs to form a quadruple. That is, the same target an
sentiment polarity pair can exist for different aspect categories and
different opinion phrases.

Although we have provided accessible links for public datasets, acquisition of
other datasets necessitates direct requests to the original authors or
proprietors. We encourage the community’s active involvement in contributing
to a live version of this review to address these identified gaps. Another
potential limitation pertains to the absence of a discussion on data ownership
and copyrights issues related to datasets obtained via web scraping. Web
scraping often contravenes the terms of service of the scraped websites, and
disseminating scraped content might infringe on copyright laws, particularly
if the scraped data have been substantially altered. Hence, it is crucial to
exercise due diligence in ensuring compliance with legal and ethical
guidelines when using such datasets.

## Acknowledgements

We thank the reviewers for their constructive feedback, which was pivotal to
the addition of a few experimental results and analysis to the paper. This
research was supported by Adobe Gift funding to the University of Houston
collaboration and by the National Science Foundation (NSF) under grant
#1910192. The authors acknowledge the use of the Sabine Cluster and the
advanced support from the Research Community Data Core at the University of
Houston to carry out the research presented here.

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##  Appendix A Appendix

Domain |  | Aspect Terms
---|---|---
#Sent | Pos | Neg | Neu | Con | Total
Laptops | 348 | 185 | 33 | 169 | 1 | 388
Mobiles | 1141 | 600 | 210 | 578 | 28 | 1416
Tablets | 1244 | 418 | 157 | 479 | 2 | 1056
Cameras | 150 | 107 | 11 | 64 | 1 | 183
Headphones | 43 | 20 | 8 | 19 | 0 | 47
Home appliances | 84 | 10 | 0 | 34 | 0 | 44
Speakers | 47 | 20 | 3 | 25 | 0 | 48
Smartwatches | 330 | 47 | 22 | 149 | 2 | 220
Televisions | 135 | 41 | 3 | 99 | 1 | 144
Mobile apps | 229 | 98 | 20 | 46 | 0 | 164
Travels | 776 | 273 | 19 | 98 | 0 | 390
Movies | 890 | 167 | 83 | 154 | 5 | 409
Overall | 5417 | 1986 | 569 | 1914 | 40 | 4509

Table 5: Hindi Multi-Domain Dataset Statistics Figure 1: Multi sentence
reviews from different platforms. Figure 2: Example of a full restaurant
review with co-references resolved in each sentence using the context from
previous sentences of the review.

Target | Aspect Cat. | Opinion Expr | Sent.
---|---|---|---
| FOOD#QLTY | incredible and perfectly prepared | Positive
asparagus | FOOD#STY_OP | NULL | Positive
|  FOOD#QLTY | nice and juicy | Positive
| FOOD#STY_OP | served with either a peppercorn sauce or red wine reduction | Positive
steak | FOOD#STY_OP | indistinguishable | Negative
|  | extremely attentive and very friendly | Positive
service from the staff | SERVICE#GEN | highlight | Positive

Table 6: Transformation of Target, Aspect, Opinion, and Sentiment using the
extended review level context, for Example in Figure 2.

###  A.1 Dataset Info Tables

Dataset Paper | Source | Domain | Lng | Stats
---|---|---|---|---
#Revs | #Sent | Target/Aspect Term
#pos | #neg | #neu
Customer Reviews Hu and Liu (2004b) | Amazon.com | Cameras | EN | 34 | 346 | 172 | 31 | -
Phones | EN | 41 | 546 | 252 | 86 | -
Electronics | EN | 95 | 1716 | 514 | 331 | -
DVD-player | EN | 99 | 739 | 195 | 235 | -
Cameras | EN | 45 | 597 | 224 | 61 | -
Customer Reviews Ding et al. (2008) | Amazon.com | Cameras | EN | 51 | 300 | 164 | 58 | -
Cameras | EN | 1 | 229 | 121 | 27 | -
Routers | EN | 31 | 312 | 186 | 79 | -
Phones | EN | 49 | 594 | 310 | 148 | -
Routers | EN | 48 | 577 | 154 | 64 | -
Ipod | EN | - | 531 | 129 | 62 | -
Mp3 player | EN | 50 | 1011 | 406 | 177 | -
Diaper Champ | EN | 48 | 375 | 183 | 56 | -
Antivirus | EN | 46 | 380 | 72 | 169 | -
JDPA Kessler et al. (2010) | J.D. Power and Associates | Cameras | EN | 96 | 3527 | - | - | -
Cars | EN | 111 | 4493 | - | - | -
Darmstadt Service Toprak et al. (2010) | tu-darmstadt.de | University reviews | EN | 240 | 2786 | - | - | -
Darmstadt car service | Service Reviews | EN | 234 | 6091 | - | - | -
Twitter Comments Dong et al. (2014) | www.twitter.com | Twitter comments | EN | - | 6940 | 1734 | 1733 | 3371
SE-14 Pontiki et al. (2014) | Amazon.com | Laptops | EN | - | 3845 | 1331 | 994 | 629
Customer Reviews Liu et al. (2015) | Amazon.com | Router | EN | - | 879 | 185 | 122 | -
Computer | EN | - | 581 | 270 | 84 | -
Speaker | EN | - | 689 | 362 | 78 | -
Hindi Multi-Domain Akhtar et al. (2018) | News-, blog-, e-com. sites |  Laptops & 11 others** | HN | - | 5417 | 1986 | 569 | 1954
MAMS Jiang et al. (2019) |  | Restaurants | EN | - | 5297 | 4183 | 3418 | 6253
TOWE Fan et al. (2019) | SE-14 Restaurants | Restaurants$\pm\pm$ | EN | - | 2127 | 3508 targ.- op. pairs
SE-15 Restaurants | Restaurants$\pm\pm$ | EN | - | 1079 | 1512 targ.- op. pairs
SE-16 Restaurants | Restaurants$\pm\pm$ | EN | - | 1408 | 1969 targ.- op. pairs
SE-14 Laptops | Laptops$\pm\pm$ | EN | - | 1501 | 2116 targ.- op. pairs
ASTE-v1 Peng et al. (2020) | SE-14 Restaurants | Restaurants$\pm\pm$ | EN | - | 2119 | 3470 | 839 | 380
SE-15 Restaurants | Restaurants$\pm\pm$ | EN | - | 1059 | 1586 | 454 | 66
SE-16 Restaurants | Restaurants$\pm\pm$ | EN | - | 1372 | 2065 | 545 | 97
SE-14 Laptops | Laptops$\pm\pm$ | EN | - | 1487 | 1664 | 1012 | 365
ASTE-v2 Xu et al. (2020) | SE-14 Restaurants | Restaurants$\pm\pm$ | EN | - | 2119 | 2769 | 756 | 286
SE-15 Restaurants | Restaurants$\pm\pm$ | EN | - | 1057 | 1285 | 401 | 61
SE-16 Restaurants | Restaurants$\pm\pm$ | EN | - | 1372 | 1674 | 483 | 90
SE-14 Laptops | Laptops$\pm\pm$ | EN | - | 1487 | 1350 | 774 | 225
ABSITA-2020 De Mattei et al. (2020) | e-Commerce platform | SD cards & 20 others$\mp\mp$ | IT | - | 4363 | 7219 | 1577 | -
ASOTE-v1 Li et al. (2021) | SE-14 Restaurants | Restaurants$\pm\pm$ | EN | - | 4828 | 2987 | 820 | 283
SE-15 Restaurants | Restaurants$\pm\pm$ | EN | - | 1741 | 1304 | 386 | 80
SE-16 Restaurants | Restaurants$\pm\pm$ | EN | - | 2355 | 1713 | 459 | 116
SE-14 Laptops | Laptops$\pm\pm$ | EN | - | 3021 | 1396 | 806 | 213
ASOTE-v2 Li et al. (2021) | SE-14 Restaurants | Restaurants$\pm\pm$ | EN | - | 6040 | 2987 | 820 | 283
SE-15 Restaurants | Restaurants$\pm\pm$ | EN | - | 2507 | 1304 | 386 | 80
SE-16 Restaurants | Restaurants$\pm\pm$ | EN | - | 3377 | 1713 | 459 | 116
SE-14 Laptops | Laptops$\pm\pm$ | EN | - | 4954 | 1392 | 806 | 213
N.MTSC Hamborg and Donnay (2021) | Financial News | News | EN | - | 3021 | 1396 | 806 | 213
DM-ASTE Xu et al. (2023) | Amazon.com Ni et al. (2019) | Electronics$\pm\pm$ | EN | - | 1994 | 5921 | 1316 | 306
Fashion$\pm\pm$ | EN | - | 1217 | 3578 | 918 | 215
Beauty$\pm\pm$ | EN | - | 766 | 2576 | 473 | 117
Home$\pm\pm$ | EN | - | 1503 | 4187 | 1091 | 180
Book$\pm\pm$ | EN | - | 484 | 1306 | 232 | 63
Pet$\pm\pm$ | EN | - | 507 | 1263 | 306 | 78
Toy$\pm\pm$ | EN | - | 527 | 1622 | 453 | 70
Grocery$\pm\pm$ | EN | - | 526 | 1597 | 285 | 80
Dom-Exp-ASTE Chia et al. (2023) | SE-16 Restaurants | Restaurants$\pm\pm$ | EN | - | 2942 | - | - | -
SE-16 Laptops | Laptops$\pm\pm$ | EN | - | 1446 | - | - | -
Hotels Angelidis et al. (2021) | Hotels$\pm\pm$ | EN | - | 2136 | - | - | -
Cosmetics He and McAuley (2016) | Cosmetics$\pm\pm$ | EN | - | 2468 | - | - | -

Table 7: Publicly available ABSA datasets with no aspect category annotations.
Lng: Language, #R: Number of Reviews, #S: Number of Sentences, #pos: Number of
positive reviews, #neg: Number of negative reviews, #neu: Number of neutral
reviews. EN: English, IT: Italian, HN: Hindi. N.MTSC: NewsMTSC Dataset. **:
Full table in Appendix Table 5. $\pm\pm$: indicates that those datasets have
the opinion phrase annotation along with other elements. $\mp\mp$: Irons,
Water Bottles, Action Cameras, Razors, Phones, Printer Cartridges, Coffee
Capsules, Backpacks, Hair Dryers, 2 different Movies, 2 different Books, Toy
Phones, Car Light bulbs, Sweatshirts, Boots, Fans, Storage Chest, Shoe
Cabinets, Personal Digital Assistants, TV streaming boxes/sticks. Note: The
domain column has a downloadable link to each dataset.

Dataset Paper | Source | Domain | Lng | Stats
---|---|---|---|---
#Revs | #Sent | Target/Aspect Term | Aspect Category
#pos | #neg | #neu | #pos | #neg | #neu
TripAdvisor Hotels Wang et al. (2011) | www.tripadvisor .com | Hotels** | EN | 108K | 1M | - | - | - | 1.63M | 153K | 178K
SE-14 Pontiki et al. (2014) | CSNY Ganu et al. (2009) | Restaurants | EN | - | 3841 | 2892 | 1001 | 829 | 2836 | 998 | 594
Steinberger et al. (2014) | www.nejezto.cz | Restaurants | CZ | - | 1244 | 679 | 725 | 403 | 521 | 569 | 246
HAAD Al-Smadi et al. (2015) | LABR book reviews | Books | AR | - | 2389 | 1376 | 1287 | 148 | 721 | 750 | 14
SE-15 Pontiki et al. (2015) | CSNY Ganu et al. (2009) | Restaurants | EN | 350 | 2000 | 1326 | 496 | 73 | 1652 | 749 | 98
Amazon.com | Laptops | EN | 450 | 2500 | - | - | - | 1644 | 1094 | 185
SE-16 Pontiki et al. (2016) | CSNY Ganu et al. (2009) | Restaurants | EN | 400 | 2286 | 1817 | 634 | 106 | 2268 | 953 | 145
Amazon.com | Laptops | EN | 530 | 3308 | - | - | - | 2118 | 1358 | 236
- | Restaurants | ES | - | 2691 | 1907 | 672 | 125 | 2675 | 948 | 168
- | Restaurants | TR | 339 | 1248 | 865 | 555 | 119 | 924 | 635 | 135
- | Telecom | TR | - | 3000 | - | - | - | - | - | -
- | Hotels | AR | 2291 | 6029 | 7213 | 4003 | 824 | 7705 | 4556 | 852
- | Restaurants | DU | 400 | 2286 | 1016 | 546 | 145 | 1431 | 857 | 185
- | Mobile Phones | DU | 270 | 1697 | - | - | - | 1454 | 225 | 110
Loukachevitch et al. (2015) | Restaurants | RU | 405 | 4699 | 3139 | 696 | 313 | 3973 | 1030 | 379
- | Restaurants | FR | 455 | 2429 | 1285 | 1061 | 289 | 1605 | 1646 | 233
- | Mobile Phones | CH | 200 | 9500 | - | - | - | 1168 | 794 | -
| - | Digital Cameras | CH | 200 | 8100 | - | - | - | 1153 | 587 | -
SentiHood Saeidi et al. (2016) | Yahoo Question Answering | Urban Neighborhoods | EN | - | 5215 | - | - | - | 4305 | 1606 | -
Customer Response Yin et al. (2017) | www.beeradvocate .com | Beer Advocate** | EN | 51K | 552K | - | - | - | 176K | 8902 | 64K
www.tripadvisor .com | Hotels** | EN | 29K | 375K | - | - | - | 120K | 66K | 49.1K
GermEval-2017 Wojatzki et al. (2017) | Internet crawling with search queries | Soc.Med., blogs, news | DE | - | 27.8K | 2802 | 12.5K | 1459 | 2815 | 12.6K | 13.9K
FiQA de França Costa and da Silva (2018) | Financial microblogs and headlines | Financial** | EN | 1303 | - | 774 | 399 | - | 774 | 399 | -
Bangla Rest., Cricket | FB, BBC, Daily Pronthom | Cricket | BG | - | 2691 | - | - | - | 571 | 2157 | 266
Rahman and Kumar Dey (2018) | SE-14 Rest Pontiki et al. (2014) | Restaurants | BG | - | 1712 | - | - | - | 477 | 1226 | 371
ABSITA-2018 Basile et al. (2018) | booking.com | Hotels | IT | - | 9285 | - | - | - | 6893 | 5288 | -
Foursquare Brun and Nikoulina (2018) | foursquare.com/ | Restaurants | EN | - | 1006 | 759 | 108 | 16 | 947 | 191 | 19
MAMS Jiang et al. (2019) | CSNY Ganu et al. (2009) | Restaurants | EN | - | 3849 | - | - | - | 2415 | 2606 | 3858
Telugu Movies Regatte et al. (2020) |  123telugu, eenadu, samayam | Movies | TE | - | 5027 | 2480 | 3251 | 1129 | 2480 | 3251 | 1129
Vietnam. Smartph. Thanh et al. (2021) | e-commerce sites | Smartphones | VI | - | 11122 | - | - | - | 21.7K | 11.2K | 2214
ASAP Bu et al. (2021) | O2O e-commerce platforms | Restaurants | CH | 46K | - | - | - | - | 169K | 35K | 66K
ASQP Zhang et al. (2021a) | SE-15 Pontiki et al. (2015) | Restaurants | EN | - | 1580 | 1407 | 489 | 68 | 1710 | 701 | 85
SE-16 Pontiki et al. (2016) | Restaurants | EN | - | 2124 | 1811 | 613 | 110 | 2229 | 877 | 135
ACOS Cai et al. (2021) | SE-16 Pontiki et al. (2016) | Restaurants | EN | - | 2287 | 2742 | 1518 | 259 | 3578 | 1879 | 316
Amazon.com | Laptops | EN | - | 4079 | 2004 | 663 | 114 | 2503 | 1007 | 151
MEMD-ABSA Cai et al. (2023) | https://nijianmo.github.io/amazon/ | Books | EN | 986 | 2967 | - | - | - | - | - | -
Ni et al. (2019) | Clothing | EN | 928 | 2373 | - | - | - | - | - | -
https://www.yelp.com/dataset/download | Restaurant | EN | 940 | 3526 | - | - | - | - | - | -
http://insideairbnb.com/get-the-data/ | Hotel | EN | 1029 | 5152 | - | - | - | - | - | -
Amazon.com | Laptops | EN | - | 4076 | - | - | - | - | - | -

Table 8: Publicly available ABSA datasets with aspect category annotations
along with aspect terms/targets and polarity. Lng: Language, #Revs: Number of
Reviews, #Sent: Number of Sentences, #pos: Number of positive reviews, #neg:
Number of negative reviews, #neu: Number of neutral reviews. EN: English, AR:
Arabic, IT: Italian, CZ: Czech, TU: Turkish, RU: Russian, FR: French, CH:
Chinese, DE: German, BG: Bangla, TE: Telugu, VI: Vietnamese, ES: Spanish, DU:
Dutch. **: indicates that the dataset has ratings converted to categorical
sentiment polarities. Note: The domain column has a downloadable link to each
dataset.