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CluSent – Combining Semantic Expansion and De-Noising for Dataset-Oriented Sentiment Analysis of Short Texts

Authors:

Washington Cunha,

Claudio Valiense,

Leonardo Rocha,

Marcos André GonçalvesAuthors Info & Claims

WebMedia '23: Proceedings of the 29th Brazilian Symposium on Multimedia and the Web

Pages 110 - 118

https://doi.org/10.1145/3617023.3617039

Published: 23 October 2023 Publication History

Abstract

The lack of sufficient information, mainly in short texts, is a major challenge to building effective sentiment models. Short texts can be enriched with more complex semantic relationships that better capture affective information, with a potential undesired side effect of noise introduced into the data. This work proposes a new strategy for customized dataset-oriented sentiment analysis – CluSent – that exploits a powerful, recently proposed concept for representing semantically related words – CluWords. CluSent tackles the issues mentioned above of information shortage and noise by: (i) exploiting the semantic neighborhood of a given pre-trained word embedding to enrich document representation and (ii) introducing dataset-oriented filtering and weighting mechanisms to cope with noise, which takes advantage of the polarity and intensity information from lexicons. In our experimental evaluation, considering 19 datasets, five state-of-the-art baselines (including modern transformer architectures), and two metrics, CluSent was the best method in 30 out of 38 possibilities, with significant gains over the strongest baselines (over 14%).

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Cited By

Viegas FCanuto SCunha WFrança CValiense CFonseca GMachado ARocha LGonçalves M(2024)Pipelining Semantic Expansion and Noise Filtering for Sentiment Analysis of Short Documents – CluSent MethodJournal on Interactive Systems10.5753/jis.2024.411715:1(561-575)Online publication date: 11-Jun-2024
https://doi.org/10.5753/jis.2024.4117
Liu YYu S(2024)Web Semantic-Enhanced Multimodal Sentiment Analysis Using Multilayer Cross-Attention FusionInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.36065320:1(1-29)Online publication date: 9-Nov-2024
https://dl.acm.org/doi/10.4018/IJSWIS.360653

Index Terms

CluSent – Combining Semantic Expansion and De-Noising for Dataset-Oriented Sentiment Analysis of Short Texts
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Sentiment analysis

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cover image ACM Other conferences

WebMedia '23: Proceedings of the 29th Brazilian Symposium on Multimedia and the Web

October 2023

285 pages

ISBN:9798400709081

DOI:10.1145/3617023

Copyright © 2023 ACM.

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New York, NY, United States

Publication History

Published: 23 October 2023

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CNPq
Fapemig
Google Research Awards
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AWS

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WebMedia '23

WebMedia '23: Brazilian Symposium on Multimedia and the Web

October 23 - 27, 2023

Ribeirão Preto, Brazil

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Cited By

Viegas FCanuto SCunha WFrança CValiense CFonseca GMachado ARocha LGonçalves M(2024)Pipelining Semantic Expansion and Noise Filtering for Sentiment Analysis of Short Documents – CluSent MethodJournal on Interactive Systems10.5753/jis.2024.411715:1(561-575)Online publication date: 11-Jun-2024
https://doi.org/10.5753/jis.2024.4117
Liu YYu S(2024)Web Semantic-Enhanced Multimodal Sentiment Analysis Using Multilayer Cross-Attention FusionInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.36065320:1(1-29)Online publication date: 9-Nov-2024
https://dl.acm.org/doi/10.4018/IJSWIS.360653

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