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

研究成果: 書貢獻/報告類型會議貢獻

摘要

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.

原文英語
主出版物標題ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing
編輯Lung-Hao Lee, Chia-Hui Chang, Kuan-Yu Chen
發行者The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
頁面210-217
頁數8
ISBN(電子)9789869576949
出版狀態已發佈 - 2021
事件33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021 - Taoyuan, 臺灣
持續時間: 10月 15 202110月 16 2021

出版系列

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

會議

會議33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021
國家/地區臺灣
城市Taoyuan
期間10/15/2110/16/21

ASJC Scopus subject areas

  • 語言與語言學
  • 語言和語言學
  • 言語和聽力

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