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Aspect-Based Sentiment Analysis (ABSA) provides a fine-grained understanding of opinions by linking sentiment to specific aspects in text. While transformer-based models excel at this task, their black-box nature limits their…

统计方法学 · 统计学 2026-02-24 Luisa Stracqualursi , Patrizia Agati

Text sentiment analysis, also known as opinion mining, is research on the calculation of people's views, evaluations, attitude and emotions expressed by entities. Text sentiment analysis can be divided into text-level sentiment analysis,…

计算与语言 · 计算机科学 2022-07-08 Tianyu Zhao , Junping Du , Zhe Xue , Ang Li , Zeli Guan

Aspect-based sentiment analysis(ABSA) is a textual analysis methodology that defines the polarity of opinions on certain aspects related to specific targets. The majority of research on ABSA is in English, with a small amount of work…

计算与语言 · 计算机科学 2023-03-13 Mohammed M. Abdelgwad , Taysir Hassan A Soliman , Ahmed I. Taloba

Understanding how urban density impacts public perceptions of urban service is important for informing livable, accessible, and equitable urban planning. Conventional methods such as surveys are limited by their sampling scope, time…

社会与信息网络 · 计算机科学 2024-04-23 Lingyao Li , Songhua Hu , Atiyya Shaw , Libby Hemphill

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

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 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) is a fine-grained type of sentiment analysis that identifies aspects and their associated opinions from a given text. With the surge of digital opinionated text data, ABSA gained increasing popularity…

计算与语言 · 计算机科学 2024-09-19 Yan Cathy Hua , Paul Denny , Katerina Taskova , Jörg Wicker

Aspect-based sentiment analysis (ABSA) is a natural language processing problem that requires analyzing user-generated reviews to determine: a) The target entity being reviewed, b) The high-level aspect to which it belongs, and c) The…

计算与语言 · 计算机科学 2023-09-22 Siva Uday Sampreeth Chebolu , Franck Dernoncourt , Nedim Lipka , Thamar Solorio

Aspect-based sentiment analysis (ABSA) and Targeted ASBA (TABSA) allow finer-grained inferences about sentiment to be drawn from the same text, depending on context. For example, a given text can have different targets (e.g., neighborhoods)…

计算与语言 · 计算机科学 2020-12-15 Zhengxuan Wu , Desmond C. Ong

Since the dawn of the digitalisation era, customer feedback and online reviews are unequivocally major sources of insights for businesses. Consequently, conducting comparative analyses of such sources has become the de facto modus operandi…

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) 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) studies the consumer opinion on the market products. It involves examining the type of sentiments as well as sentiment targets expressed in product reviews. Analyzing the language used in a review is a…

计算与语言 · 计算机科学 2021-03-02 Akbar Karimi , Leonardo Rossi , Andrea Prati

As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis (ABSA), aiming to analyze and understand people's opinions at the aspect level, has been attracting considerable interest in the last decade. To handle…

计算与语言 · 计算机科学 2022-11-08 Wenxuan Zhang , Xin Li , Yang Deng , Lidong Bing , Wai Lam

Aspect-based Sentiment Analysis (ABSA) seeks to predict the sentiment polarity of a sentence toward a specific aspect. Recently, it has been shown that dependency trees can be integrated into deep learning models to produce the…

计算与语言 · 计算机科学 2020-10-27 Amir Pouran Ben Veyseh , Nasim Nour , Franck Dernoncourt , Quan Hung Tran , Dejing Dou , Thien Huu Nguyen

Aspect based sentiment analysis (ABSA) can provide more detailed information than general sentiment analysis, because it aims to predict the sentiment polarities of the given aspects or entities in text. We summarize previous approaches…

计算与语言 · 计算机科学 2018-05-21 Wei Xue , Tao Li

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) predicts sentiment polarity towards a specific aspect in the given sentence. While pre-trained language models such as BERT have achieved great success, incorporating dynamic semantic changes into ABSA…

计算与语言 · 计算机科学 2022-11-24 Kai Zhang , Kun Zhang , Mengdi Zhang , Hongke Zhao , Qi Liu , Wei Wu , Enhong Chen
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