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Aspect-based sentiment analysis with component focusing multi-head co-attention networks.

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Title: Aspect-based sentiment analysis with component focusing multi-head co-attention networks.
Authors: Cheng, Li-Chen1 (AUTHOR) jessicacheng@mail.ntut.edu.tw, Chen, Yen-Liang2 (AUTHOR), Liao, Yuan-Yu2 (AUTHOR)
Source: Neurocomputing. Jun2022, Vol. 489, p9-17. 9p.
Subjects: Sentiment analysis, User-generated content, Task analysis, Customer experience, Statistical weighting, Information resources
Abstract: User-generated content based on customer opinions and experience has become a rich source of valuable information for enterprises. The purpose of aspect-based sentiment analysis is to predict the sentiment polarity of specific targets from user-generated content. This study proposes a component focusing multi-head co-attention network model which contains three modules: extended context, component focusing, and multi-headed co-attention, designed to improve upon problems encountered in the past. The extended context module improves the ability of bidirectional encoder representations from transformers to handle aspect-based sentiment analysis tasks, and the component focusing module improves the weighting of adjectives and adverbs, to alleviate the problem of average pooling, which treats every word as an equally important term. The multi-head co-attention network is applied to learn the important words in a multi-word target before acquiring the context representation and performs the attention mechanism on the sequence data. The performance of the proposed model is evaluated in extensive experiments on publicly available datasets. The results show that the performance of the proposed model is better than that of the recent state-of-the-art models. [ABSTRACT FROM AUTHOR]
Copyright of Neurocomputing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: Aspect-based sentiment analysis with component focusing multi-head co-attention networks.
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  Data: <searchLink fieldCode="JN" term="%22Neurocomputing%22">Neurocomputing</searchLink>. Jun2022, Vol. 489, p9-17. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Sentiment+analysis%22">Sentiment analysis</searchLink><br /><searchLink fieldCode="DE" term="%22User-generated+content%22">User-generated content</searchLink><br /><searchLink fieldCode="DE" term="%22Task+analysis%22">Task analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Customer+experience%22">Customer experience</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+weighting%22">Statistical weighting</searchLink><br /><searchLink fieldCode="DE" term="%22Information+resources%22">Information resources</searchLink>
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  Label: Abstract
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  Data: User-generated content based on customer opinions and experience has become a rich source of valuable information for enterprises. The purpose of aspect-based sentiment analysis is to predict the sentiment polarity of specific targets from user-generated content. This study proposes a component focusing multi-head co-attention network model which contains three modules: extended context, component focusing, and multi-headed co-attention, designed to improve upon problems encountered in the past. The extended context module improves the ability of bidirectional encoder representations from transformers to handle aspect-based sentiment analysis tasks, and the component focusing module improves the weighting of adjectives and adverbs, to alleviate the problem of average pooling, which treats every word as an equally important term. The multi-head co-attention network is applied to learn the important words in a multi-word target before acquiring the context representation and performs the attention mechanism on the sequence data. The performance of the proposed model is evaluated in extensive experiments on publicly available datasets. The results show that the performance of the proposed model is better than that of the recent state-of-the-art models. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Neurocomputing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.neucom.2022.03.027
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      – Code: eng
        Text: English
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        PageCount: 9
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      – SubjectFull: Sentiment analysis
        Type: general
      – SubjectFull: User-generated content
        Type: general
      – SubjectFull: Task analysis
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      – SubjectFull: Customer experience
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      – SubjectFull: Statistical weighting
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      – SubjectFull: Information resources
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      – TitleFull: Aspect-based sentiment analysis with component focusing multi-head co-attention networks.
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            – D: 07
              M: 06
              Text: Jun2022
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              Y: 2022
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