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相关论文: Enhancing Aspect-based Sentiment Analysis in Touri…

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This study advances aspect-based sentiment analysis (ABSA) for Persian-language user reviews in the tourism domain, addressing challenges of low-resource languages. We propose a hybrid BERT-based model with Top-K routing and auxiliary…

计算与语言 · 计算机科学 2026-02-16 Hamidreza Kazemi Taskooh , Taha Zare Harofte

Aspect-based sentiment analysis (ASBA) is a refined approach to sentiment analysis that aims to extract and classify sentiments based on specific aspects or features of a product, service, or entity. Unlike traditional sentiment analysis,…

计算与语言 · 计算机科学 2025-01-16 Karukriti Kaushik Ghosh , Chiranjib Sur

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 increasingly crucial in Natural Language Processing (NLP) for applications such as customer feedback analysis and product recommendation systems. ABSA goes beyond traditional sentiment analysis by…

计算与语言 · 计算机科学 2024-10-29 Adamu Lawan , Juhua Pu , Haruna Yunusa , Jawad Muhammad , Aliyu Umar

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

Aspect-Based Sentiment Analysis (ABSA) aims to identify terms or multiword expressions (MWEs) on which sentiments are expressed and the sentiment polarities associated with them. The development of supervised models has been at the…

计算与语言 · 计算机科学 2024-03-27 Gaurav Negi , Rajdeep Sarkar , Omnia Zayed , Paul Buitelaar

Aspect-based sentiment analysis (ABSA) delves into understanding sentiments specific to distinct elements within a user-generated review. It aims to analyze user-generated reviews to determine a) the target entity being reviewed, b) the…

计算与语言 · 计算机科学 2024-03-07 Siva Uday Sampreeth Chebolu , Franck Dernoncourt , Nedim Lipka , Thamar Solorio

Aspect-based sentiment analysis (ABSA) is an important subtask of sentiment analysis, which aims to extract the aspects and predict their sentiments. Most existing studies focus on improving the performance of the target domain by…

计算与语言 · 计算机科学 2024-05-10 Xuanwen Ding , Jie Zhou , Liang Dou , Qin Chen , Yuanbin Wu , Chengcai Chen , Liang He

In recent years, aspect-based sentiment analysis (ABSA) has made rapid progress and shown strong practical value. However, existing research and benchmarks are largely concentrated on high-resource languages, leaving fine-grained sentiment…

计算与语言 · 计算机科学 2026-04-14 Aizihaierjiang Yusufu , Jiang Liu , Kamran Aziz , Abidan Ainiwaer , Bobo Li , Fei Li , Donghong Ji , Aizierguli Yusufu

Aspect-based sentiment analysis (ABSA) tries to predict the polarity of a given document with respect to a given aspect entity. While neural network architectures have been successful in predicting the overall polarity of sentences,…

计算与语言 · 计算机科学 2017-12-18 Yi Tay , Anh Tuan Luu , Siu Cheung Hui

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) is a fine-grained sentiment analysis task that aims to identify sentiment toward specific aspects of an entity. While large language models (LLMs) have shown strong performance in various natural…

计算与语言 · 计算机科学 2025-08-12 Jakub Šmíd , Pavel Přibáň , Pavel Král

This paper introduces a novel Czech dataset in the restaurant domain for aspect-based sentiment analysis (ABSA), enriched with annotations of opinion terms. The dataset supports three distinct ABSA tasks involving opinion terms,…

计算与语言 · 计算机科学 2026-03-05 Jakub Šmíd , Pavel Přibáň , Pavel Král

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) is a fine-grained sentiment evaluation task, which analyzes the emotional polarity of the evaluation aspects. Generally, the emotional polarity of an aspect exists in the corresponding opinion…

计算与语言 · 计算机科学 2023-10-10 Dongming Wu , Lulu Wen , Chao Chen , Zhaoshu Shi

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) 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) 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…

Aspect based sentiment analysis (ABSA) deals with the identification of the sentiment polarity of a review sentence towards a given aspect. Deep Learning sequential models like RNN, LSTM, and GRU are current state-of-the-art methods for…

计算与语言 · 计算机科学 2022-08-05 Ashish Kumar , Vasundhra Dahiya , Aditi Sharan

Aspect-based sentiment analysis (ABSA) involves identifying sentiment towards specific aspect terms in a sentence and allows us to uncover nuanced perspectives and attitudes on particular aspects of a product, service, or topic. However,…

计算与语言 · 计算机科学 2024-09-18 Lingling Xu , Haoran Xie , S. Joe Qin , Fu Lee Wang , Xiaohui Tao
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