Results 241 to 250 of about 24,177,706 (292)
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Aspect-based sentiment classification with multi-attention network
Neurocomputing, 2020Abstract 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
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Fine-Tuning for Cross-Domain Aspect-Based Sentiment Classification
IEEE/WIC/ACM International Conference on Web Intelligence, 2021Aspect-Based Sentiment Classification (ABSC) is a subfield of sentiment analysis concerned with classifying sentiment attributed to pre-identified aspects. A problem in ABSC nowadays is the limited availability of labeled data for certain domains. This study aims to improve sentiment classification accuracy for these domains where labeled data is ...
Stefan van Berkum +4 more
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Aspect-Based Twitter Sentiment Classification
2013 IEEE 25th International Conference on Tools with Artificial Intelligence, 2013Due 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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Structural Position Network for Aspect-Based Sentiment Classification
2020Aspect-based sentiment classification aims to discriminate the polarity of each aspect term for a given sentence. Previous works mainly focus on sequential modeling and aspect representations. However, the syntactical information and relative structural position of aspect in sentence are neglected, resulting in some irrelevant contextual words as clues
Pu Song +5 more
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Aspect Based Sentiment Analysis: Category Detection and Sentiment Classification for Hindi
2018E-commerce markets in developing countries (e.g. India) have witnessed a tremendous amount of user’s interest recently. Product reviews are now being generated daily in huge amount. Classifying the sentiment expressed in a user generated text/review into certain categories of interest, for example, positive or negative is famously known as sentiment ...
Md. Shad Akhtar +2 more
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Aspect-Based Sentiment Classification with Reinforcement Learning and Local Understanding
2021Aspect-based sentiment analysis is a fine-grained classification task in natural language processing. In this paper, we propose a new framework with reinforcement learning agent to assess the importance of words in the sentence for sentiment analysis and mask out insignificant ones.
Ming-Fan Li +4 more
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Predictive aspect-based sentiment classification of online tourist reviews
Journal of Information Science, 2018With the increase of online tourists reviews, discovering sentimental idea regarding a tourist place through the posted reviews is becoming a challenging task. The presence of various aspects discussed in user reviews makes it even harder to accurately extract and classify the sentiments.
Muhammad Afzaal +2 more
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A Context-focused Attention Evolution Model for Aspect-based Sentiment Classification
ACM Transactions on Asian and Low-Resource Language Information Processing, 2023Due to their inherent capability in the semantic alignment of aspects and their context words, Attention and Long-Short-Term-Memory (LSTM) mechanisms are widely adopted for Aspect-Based Sentiment Classification (ABSC) tasks. Instead, it is challenging to handle long-range word dependencies on multiple entities due to the deficiency in attention ...
Xiaoheng Deng +2 more
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Adversarial Sample Generation for Aspect based Sentiment Classification
Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022, 2022Mamta, Asif Ekbal
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Aspect-based Sentiment Classification via Reinforcement Learning
2021 IEEE International Conference on Data Mining (ICDM), 2021Lichen Wang +8 more
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