Discriminative features fusion with bert for social sentiment analysis

Duy Duc Le Nguyen; Yen Chun Huang; Yung Chun Chang

doi:10.1007/978-3-030-55789-8_3

Discriminative features fusion with bert for social sentiment analysis

Duy Duc Le Nguyen, Yen Chun Huang, Yung Chun Chang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

The need for sentiment analysis in social networks is increasing. In recent years, many studies have shifted from author sentiment research to reader sentiment research. However, the use of words that hinders sentiment analysis is very diverse. In this paper, we provide a model that combines the latest and most recent contextual text embedding technology and feature selection to more accurately detect the emotional intent of an article. We named it DF2BERT (Discriminative Features Fusion with Bert), and extensively applied datasets in different languages and different text classification tasks to validate our method, and compared it with several well-known approaches. Experimental results show that our model can effectively predict sentiment behind the text which outperform comparisons.

Original language	English
Title of host publication	Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Proceedings
Editors	Hamido Fujita, Jun Sasaki, Philippe Fournier-Viger, Moonis Ali
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	30-35
Number of pages	6
ISBN (Print)	9783030557881
DOIs	https://doi.org/10.1007/978-3-030-55789-8_3
Publication status	Published - 2020
Event	33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020 - Kitakyushu, Japan Duration: Sept 22 2020 → Sept 25 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12144 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020
Country/Territory	Japan
City	Kitakyushu
Period	9/22/20 → 9/25/20

Keywords

Deep neural network
Feature fusion
Natural language processing
Sentiment analysis
Text representation

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-55789-8_3

Cite this

Nguyen, D. D. L., Huang, Y. C., & Chang, Y. C. (2020). Discriminative features fusion with bert for social sentiment analysis. In H. Fujita, J. Sasaki, P. Fournier-Viger, & M. Ali (Eds.), Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Proceedings (pp. 30-35). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12144 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-55789-8_3

Discriminative features fusion with bert for social sentiment analysis. / Nguyen, Duy Duc Le; Huang, Yen Chun; Chang, Yung Chun.
Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Proceedings. ed. / Hamido Fujita; Jun Sasaki; Philippe Fournier-Viger; Moonis Ali. Springer Science and Business Media Deutschland GmbH, 2020. p. 30-35 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12144 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nguyen, DDL, Huang, YC & Chang, YC 2020, Discriminative features fusion with bert for social sentiment analysis. in H Fujita, J Sasaki, P Fournier-Viger & M Ali (eds), Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12144 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 30-35, 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Kitakyushu, Japan, 9/22/20. https://doi.org/10.1007/978-3-030-55789-8_3

Nguyen DDL, Huang YC, Chang YC. Discriminative features fusion with bert for social sentiment analysis. In Fujita H, Sasaki J, Fournier-Viger P, Ali M, editors, Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 30-35. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-55789-8_3

Nguyen, Duy Duc Le ; Huang, Yen Chun ; Chang, Yung Chun. / Discriminative features fusion with bert for social sentiment analysis. Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Proceedings. editor / Hamido Fujita ; Jun Sasaki ; Philippe Fournier-Viger ; Moonis Ali. Springer Science and Business Media Deutschland GmbH, 2020. pp. 30-35 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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N2 - The need for sentiment analysis in social networks is increasing. In recent years, many studies have shifted from author sentiment research to reader sentiment research. However, the use of words that hinders sentiment analysis is very diverse. In this paper, we provide a model that combines the latest and most recent contextual text embedding technology and feature selection to more accurately detect the emotional intent of an article. We named it DF2BERT (Discriminative Features Fusion with Bert), and extensively applied datasets in different languages and different text classification tasks to validate our method, and compared it with several well-known approaches. Experimental results show that our model can effectively predict sentiment behind the text which outperform comparisons.

AB - The need for sentiment analysis in social networks is increasing. In recent years, many studies have shifted from author sentiment research to reader sentiment research. However, the use of words that hinders sentiment analysis is very diverse. In this paper, we provide a model that combines the latest and most recent contextual text embedding technology and feature selection to more accurately detect the emotional intent of an article. We named it DF2BERT (Discriminative Features Fusion with Bert), and extensively applied datasets in different languages and different text classification tasks to validate our method, and compared it with several well-known approaches. Experimental results show that our model can effectively predict sentiment behind the text which outperform comparisons.

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Discriminative features fusion with bert for social sentiment analysis

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