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Aspect-based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task which involves four elements from user-generated texts: aspect term, aspect category, opinion term, and sentiment polarity. Most computational approaches focus…

We introduce InstructABSA, an instruction learning paradigm for Aspect-Based Sentiment Analysis (ABSA) subtasks. Our method introduces positive, negative, and neutral examples to each training sample, and instruction tune the model…

计算与语言 · 计算机科学 2023-11-14 Kevin Scaria , Himanshu Gupta , Siddharth Goyal , Saurabh Arjun Sawant , Swaroop Mishra , Chitta Baral

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task which aims to extract the aspects from sentences and identify their corresponding sentiments. Aspect term extraction (ATE) is the crucial step for ABSA. Due to…

计算与语言 · 计算机科学 2022-02-01 Xiang Chen , Xiaojun Wan

Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained opinion polarity towards a specific aspect, is a challenging subtask of sentiment analysis (SA). In this paper, we construct an auxiliary sentence from the aspect…

计算与语言 · 计算机科学 2019-03-25 Chi Sun , Luyao Huang , Xipeng Qiu

Aspect-based sentiment analysis (ABSA) is an emerging fine-grained sentiment analysis task that aims to extract aspects, classify corresponding sentiment polarities and find opinions as the causes of sentiment. The latest research tends to…

计算与语言 · 计算机科学 2021-09-20 Chengxi Li , Feiyu Gao , Jiajun Bu , Lu Xu , Xiang Chen , Yu Gu , Zirui Shao , Qi Zheng , Ningyu Zhang , Yongpan Wang , Zhi Yu

Aspect-based sentiment analysis (ABSA), a fine-grained sentiment classification task, has received much attention recently. Many works investigate sentiment information through opinion words, such as ''good'' and ''bad''. However, implicit…

计算与语言 · 计算机科学 2023-12-19 Jihong Ouyang , Zhiyao Yang , Silong Liang , Bing Wang , Yimeng Wang , Ximing Li

In aspect-based sentiment analysis (ABSA), many neural models are equipped with an attention mechanism to quantify the contribution of each context word to sentiment prediction. However, such a mechanism suffers from one drawback: only a…

计算与语言 · 计算机科学 2021-03-08 Jinsong Su , Jialong Tang , Hui Jiang , Ziyao Lu , Yubin Ge , Linfeng Song , Deyi Xiong , Le Sun , Jiebo Luo

Aspect-based sentiment analysis (ABSA) aims to associate a text with a set of aspects and infer their respective sentimental polarities. State-of-the-art approaches are built on fine-tuning pre-trained language models, focusing on learning…

计算与语言 · 计算机科学 2024-08-26 Murtadha Ahmed , Bo Wen , Shengfeng Pan , Jianlin Su , Luo Ao , Yunfeng Liu

Aspect Based Sentiment Analysis (ABSA) is the sub-field of Natural Language Processing that deals with essentially splitting our data into aspects ad finally extracting the sentiment information. ABSA is known to provide more information…

计算与语言 · 计算机科学 2020-06-09 Kaustubh Yadav

Aspect-based sentiment analysis (ABSA) identifies sentiment information related to specific aspects and provides deeper market insights to businesses and organizations. With the emergence of large language models (LMs), recent studies have…

计算与语言 · 计算机科学 2024-05-30 Guangmin Zheng , Jin Wang , Liang-Chih Yu , Xuejie Zhang

Aspect-based sentiment analysis (ABSA), a popular research area in NLP has two distinct parts -- aspect extraction (AE) and labeling the aspects with sentiment polarity (ALSA). Although distinct, these two tasks are highly correlated. The…

Aspect-based Sentiment Analysis (ABSA) is a fine-grained opinion mining approach that identifies and classifies opinions associated with specific entities (aspects) or their categories within a sentence. Despite its rapid growth and broad…

计算与语言 · 计算机科学 2025-11-06 Yan Cathy Hua , Paul Denny , Jörg Wicker , Katerina Taškova

We explore how weak supervision on abundant unlabeled data can be leveraged to improve few-shot performance in aspect-based sentiment analysis (ABSA) tasks. We propose a pipeline approach to construct a noisy ABSA dataset, and we use it to…

Aspect-based Sentiment Analysis (ABSA) aims to identify the aspect terms, their corresponding sentiment polarities, and the opinion terms. There exist seven subtasks in ABSA. Most studies only focus on the subsets of these subtasks, which…

计算与语言 · 计算机科学 2021-06-09 Hang Yan , Junqi Dai , Tuo ji , Xipeng Qiu , Zheng Zhang

Aspect-based Sentiment Analysis (ABSA) is a critical task in Natural Language Processing (NLP) that focuses on extracting sentiments related to specific aspects within a text, offering deep insights into customer opinions. Traditional…

Aspect-Based Sentiment Analysis (ABSA) enables fine-grained opinion analysis by identifying sentiments toward specific aspects or targets within a text. While ABSA has been widely studied for English, research on other languages such as…

Aspect-based sentiment analysis (ABSA) is an NLP task that entails processing user-generated reviews to determine (i) the target being evaluated, (ii) the aspect category to which it belongs, and (iii) the sentiment expressed towards the…

计算与语言 · 计算机科学 2022-04-12 Siva Uday Sampreeth Chebolu , Franck Dernoncourt , Nedim Lipka , Thamar Solorio

Aspect Based Sentiment Analysis (ABSA) is the task of identifying sentiment polarity of a text given another text segment or aspect. In ABSA, a text can have multiple sentiments depending upon each aspect. Aspect Term Sentiment Analysis…

计算与语言 · 计算机科学 2020-05-05 Avinash Madasu , Vijjini Anvesh Rao

Aspect-based sentiment analysis (ABSA) aims at automatically inferring the specific sentiment polarities toward certain aspects of products or services behind the social media texts or reviews, which has been a fundamental application to…

计算与语言 · 计算机科学 2023-05-22 Hao Fei , Tat-Seng Chua , Chenliang Li , Donghong Ji , Meishan Zhang , Yafeng Ren

Aspect-based sentiment analysis (ABSA) is a more detailed task in sentiment analysis, by identifying opinion polarity toward a certain aspect in a text. This method is attracting more attention from the community, due to the fact that it…

计算与语言 · 计算机科学 2020-12-15 H. Jafarian , A. H. Taghavi , A. Javaheri , R. Rawassizadeh
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