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Syntactic denoising and multi-strategy auxiliary enhancement for aspect-based sentiment analysis. [PDF]

PLoS One
Liu L, Li D, Yue C, Gao X, Zhu Y.
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Relation construction for aspect-level sentiment classification

Information Sciences, 2022
Abstract Aspect-level sentiment classification aims to obtain fine-grained sentiment polarities of different aspects in one sentence. Most existing approaches handle the classification by acquiring the importance of context words towards each given aspect individually, and ignore the benefits brought by aspect relations.
Tianyi Liu, Jiantao Zhou, Jiandian Zeng   +2 more
exaly   +2 more sources

Detecting Dependency-Related Sentiment Features for Aspect-Level Sentiment Classification

IEEE Transactions on Affective Computing, 2023
Aspect level sentiment classification aims to classify the sentiment polarity of a given aspect term or aspect category in a sentence. For sentiment classification towards a given aspect term, since a sentence may contain more than one aspect term, there may exist some opinions which are not the modifiers of the given aspect term.
Jingyun Xu, Yi Cai, Changxi Zhu
exaly   +2 more sources

Aspect-based sentiment classification with multi-attention network

Neurocomputing, 2020
Abstract Aspect-based sentiment classification aims to predict the sentiment polarity of an aspect term in a sentence instead of the sentiment polarity of the entire sentence. Neural networks have been used for this task, and most existing methods have adopted sequence models, which require more training time than other models.
Tao Dai
exaly   +2 more sources

Aspect sentiment learning for Aspect-Level Sentiment Classification

Neural Networks, 2023
Aspect-Level Sentiment Classification (ALSC) is a fine-grained Sentiment Analysis (SA) task that aims to determine the sentiments of a sentence toward different aspects. Despite their significant success, most existing methods derive aspect sentiment semantics from individual sentences, overlooking the interrelationships among relevant sentences that ...
Zhongquan Jian, Jiajian Li, Meihong Wang, Junfeng Yao, Qingqiang Wu   +4 more
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Aspect Sentiment Classification with Document-level Sentiment Preference Modeling

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020
In the literature, existing studies always consider Aspect Sentiment Classification (ASC) as an independent sentence-level classification problem aspect by aspect, which largely ignore the document-level sentiment preference information, though obviously such information is crucial for alleviating the information deficiency problem in ASC.
Xiao Chen, Changlong Sun, Jingjing Wang, Shoushan Li, Luo Si, Min Zhang 0005, Guodong Zhou   +6 more
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Aspect-Based Twitter Sentiment Classification

2013 IEEE 25th International Conference on Tools with Artificial Intelligence, 2013
Due to the popularity of Twitter, sentiment classification for Twitter has become a hot research topic. Previous studies have approached the problem as a tweet-level classification task where each tweet is classified as positive, negative or neutral. However, getting an overall sentiment might not be useful to organizations which are using twitter for ...
Hsiang Hui Lek, Danny C. C. Poo
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Hybrid sentiment classification on twitter aspect-based sentiment analysis

Applied Intelligence, 2017
Social media sites and applications, including Facebook, YouTube, Twitter and blogs, have become major social media attractions today. The huge amount of information from this medium has become an attractive resource for organisations to monitor the opinions of users, and therefore, it is receiving a lot of attention in the field of sentiment analysis.
Zainuddin, Nurulhuda, Selamat, Ali, Ibrahim, Roliana   +2 more
openaire   +2 more sources

Knowledge Injection for Aspect-Based Sentiment Classification

2023
Since the increase of Web reviews of products and services, Aspect-Based Sentiment Classification (ABSC) has become more important to determine the sentiment of online opinions. Useful information extracted from these reviews can then be used by companies themselves, but can also be applicable by consumers.
Romany Dekker, Danae Gielisse, Chaya Jaggan, Sander Meijers, Flavius Frasincar   +4 more
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