Using Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Soeial Media Produet Reviews

Yu Ya Cheng; Wen Chao Yeh; Yan Ming Chen; Yung Chun Chang

Using Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Soeial Media Produet Reviews

Yu Ya Cheng, Wen Chao Yeh, Yan Ming Chen, Yung Chun Chang

Graduate Institute of Data Science

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

2 Citations (Scopus)

Abstract

With the popularity of the current Internet age, online social platforms have provided a bridge for communication between private companies, public organizations, and the public. The purpose of this research is to understand the user's experience of the product by analyzing product review data in different fields. We propose a BiLSTM-based neural network which infused rich emotional information. In addition to consider Valence and Arousal which is the smallest morpheme of emotional information, the dependence relationship between texts is also integrated into the deep learning model to analyze the sentiment. The experimental results show that this research can achieve good performance in predicting the vocabulary Valence and Arousal. In addition, the integration of VA and dependency information into the BiLSTM model can have excellent performance for social text sentiment analysis, which verifies that this model is effective in emotion recognition of social medial short text.

Original language	English
Title of host publication	ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing
Editors	Lung-Hao Lee, Chia-Hui Chang, Kuan-Yu Chen
Publisher	The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
Pages	210-217
Number of pages	8
ISBN (Electronic)	9789869576949
Publication status	Published - 2021
Event	33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021 - Taoyuan, Taiwan Duration: Oct 15 2021 → Oct 16 2021

Publication series

Name	ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing

Conference

Conference	33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021
Country/Territory	Taiwan
City	Taoyuan
Period	10/15/21 → 10/16/21

Keywords

Sentiment Analysis
Social Media
Valence & Arousal

ASJC Scopus subject areas

Language and Linguistics
Linguistics and Language
Speech and Hearing

Cite this

Cheng, Y. Y., Yeh, W. C., Chen, Y. M., & Chang, Y. C. (2021). Using Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Soeial Media Produet Reviews. In L.-H. Lee, C.-H. Chang, & K.-Y. Chen (Eds.), ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (pp. 210-217). (ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing). The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).

Using Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Soeial Media Produet Reviews. / Cheng, Yu Ya; Yeh, Wen Chao; Chen, Yan Ming et al.
ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. ed. / Lung-Hao Lee; Chia-Hui Chang; Kuan-Yu Chen. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), 2021. p. 210-217 (ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing).

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

Cheng, YY, Yeh, WC, Chen, YM & Chang, YC 2021, Using Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Soeial Media Produet Reviews. in L-H Lee, C-H Chang & K-Y Chen (eds), ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing, The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), pp. 210-217, 33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021, Taoyuan, Taiwan, 10/15/21.

Cheng YY, Yeh WC, Chen YM, Chang YC. Using Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Soeial Media Produet Reviews. In Lee LH, Chang CH, Chen KY, editors, ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP). 2021. p. 210-217. (ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing).

Cheng, Yu Ya ; Yeh, Wen Chao ; Chen, Yan Ming et al. / Using Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Soeial Media Produet Reviews. ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. editor / Lung-Hao Lee ; Chia-Hui Chang ; Kuan-Yu Chen. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), 2021. pp. 210-217 (ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing).

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abstract = "With the popularity of the current Internet age, online social platforms have provided a bridge for communication between private companies, public organizations, and the public. The purpose of this research is to understand the user's experience of the product by analyzing product review data in different fields. We propose a BiLSTM-based neural network which infused rich emotional information. In addition to consider Valence and Arousal which is the smallest morpheme of emotional information, the dependence relationship between texts is also integrated into the deep learning model to analyze the sentiment. The experimental results show that this research can achieve good performance in predicting the vocabulary Valence and Arousal. In addition, the integration of VA and dependency information into the BiLSTM model can have excellent performance for social text sentiment analysis, which verifies that this model is effective in emotion recognition of social medial short text.",

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PY - 2021

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AB - With the popularity of the current Internet age, online social platforms have provided a bridge for communication between private companies, public organizations, and the public. The purpose of this research is to understand the user's experience of the product by analyzing product review data in different fields. We propose a BiLSTM-based neural network which infused rich emotional information. In addition to consider Valence and Arousal which is the smallest morpheme of emotional information, the dependence relationship between texts is also integrated into the deep learning model to analyze the sentiment. The experimental results show that this research can achieve good performance in predicting the vocabulary Valence and Arousal. In addition, the integration of VA and dependency information into the BiLSTM model can have excellent performance for social text sentiment analysis, which verifies that this model is effective in emotion recognition of social medial short text.

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Using Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Soeial Media Produet Reviews

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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