A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

Zhang, Wenxuan; Li, Xin; Yang, Daoguo; Lam, Wai

doi:10.48550/arxiv.2203.01054

Cited by 13 publications

(11 citation statements)

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“…ABSA is a sub-field of sentiment analysis that identifies the sentiment with respect to specific aspects of a subject of concern [6,32]. This task involves four elements: aspect term, aspect category, opinion term, and sentiment polarity [33].…”

Section: Aspect-based Sentiment Analysis and Aspect-based Emotion Ana...mentioning

confidence: 99%

Advancements in Korean Emotion Classification: A Comparative Approach Using Attention Mechanism

Kang,

Choi,

Kim

2024

Mathematics

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Recently, the analysis of emotions in social media has been considered a significant NLP task in digital and social-media-driven environments due to their pervasive influence on communication, culture, and consumer behavior. In particular, the task of Aspect-Based Emotion Analysis (ABEA), which involves analyzing the emotions of various targets within a single sentence, has drawn attention to understanding complex and sophisticated human language. However, ABEA is a challenging task in languages with limited data and complex linguistic properties, such as Korean, which follows spiral thought patterns and has agglutinative characteristics. Therefore, we propose a Korean Target-Attention-Based Emotion Classifier (KOTAC) designed to utilize target information by unveiling emotions buried within intricate Korean language patterns. In the experiment section, we compare various methods of utilizing and representing vectors of target information for the attention mechanism. Specifically, our final model, KOTAC, shows a performance enhancement on the MTME (Multiple Targets Multiple Emotions) samples, which include multiple targets and distinct emotions within a single sentence, achieving a 0.72% increase in F1 score over a baseline model without effective target utilization. This research contributes to the development of Korean language models that better reflect syntactic features by innovating methods to not only obtain but also utilize target-focused representations.

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Section: Aspect-based Sentiment Analysis and Aspect-based Emotion Ana...mentioning

confidence: 99%

Advancements in Korean Emotion Classification: A Comparative Approach Using Attention Mechanism

Kang,

Choi,

Kim

2024

Mathematics

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“…For example, in the sentence I love the pizza at this restaurant, but the service is terrible, there are different sentiments toward pizza and service: positive and negative, respectively. The words pizza and service are called aspect terms (for more detail, see Zhang et al, 2022).…”

Section: Aspect-based Sentiment Analysismentioning

confidence: 99%

Revered and reviled: a sentiment analysis of female and male referents in three languages

Levshina,

Koptjevskaja-Tamm,

Östling

2024

Front. Commun.

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Our study contributes to the less explored domain of lexical typology, focusing on semantic prosody and connotation. Semantic derogation, or pejoration of nouns referring to women, whereby such words acquire connotations and further denotations of social pejoration, immorality and/or loose sexuality, has been a very prominent question in studies on gender and language (change). It has been argued that pejoration emerges due to the general derogatory attitudes toward female referents. However, the evidence for systematic differences in connotations of female- vs. male-related words is fragmentary and often fairly impressionistic; moreover, many researchers argue that expressed sentiments toward women (as well as men) often are ambivalent. One should also expect gender differences in connotations to have decreased in the recent years, thanks to the advances of feminism and social progress. We test these ideas in a study of positive and negative connotations of feminine and masculine term pairs such as woman - man, girl - boy, wife - husband, etc. Sentences containing these words were sampled from diachronic corpora of English, Chinese and Russian, and sentiment scores for every word were obtained using two systems for Aspect-Based Sentiment Analysis: PyABSA, and OpenAI’s large language model GPT-3.5. The Generalized Linear Mixed Models of our data provide no indications of significantly more negative sentiment toward female referents in comparison with their male counterparts. However, some of the models suggest that female referents are more infrequently associated with neutral sentiment than male ones. Neither do our data support the hypothesis of the diachronic convergence between the genders. In sum, results suggest that pejoration is unlikely to be explained simply by negative attitudes to female referents in general.

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“…ABSA is a broad research area which includes various tasks. Schouten and Frasincar (2016); Zhang et al (2022a) provided comprehensive survey to these subtasks. In this paper, due to space limitation, we only review the related tasks.…”

Section: Related Workmentioning

confidence: 99%

A Sequence-to-Structure Approach to Document-level Targeted Sentiment Analysis

Song,

Cai,

Xia

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Most previous studies on aspect-based sentiment analysis (ABSA) were carried out at the sentence level, while the research of documentlevel ABSA has not received enough attention. In this work, we focus on the document-level targeted sentiment analysis task, which aims to extract the opinion targets consisting of multilevel entities from a review document and predict their sentiments. We propose a Sequenceto-Structure (Seq2Struct) approach to address the task, which is able to explicitly model the hierarchical structure among multiple opinion targets in a document, and capture the longdistance dependencies among affiliated entities across sentences. In addition to the existing Seq2Seq approach, we further construct four strong baselines with different pretrained models. Experimental results on six domains show that our Seq2Struct approach outperforms all the baselines significantly. Aside from the performance advantage in outputting the multilevel target-sentiment pairs, our approach has another significant advantage -it can explicitly display the hierarchical structure of the opinion targets within a document. Our source code is publicly released at https://github.com/ NUSTM/Doc-TSA-Seq2Struct.

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A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

Cited by 13 publications

References 154 publications

Advancements in Korean Emotion Classification: A Comparative Approach Using Attention Mechanism

Advancements in Korean Emotion Classification: A Comparative Approach Using Attention Mechanism

Revered and reviled: a sentiment analysis of female and male referents in three languages

A Sequence-to-Structure Approach to Document-level Targeted Sentiment Analysis

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