Sentiment Analysis Using Residual Learning with Simplified CNN Extractor

Nguyen Khai Thinh, Cao Hong Nga, Yuan Shan Lee, Meng Lun Wu, Pao Chi Chang, Jia Ching Wang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

5 引文 斯高帕斯(Scopus)

摘要

Sentiment analysis has an important role in social media monitoring as it extracts public opinions, emotions, and feelings about certain products or services. There are several publications in building a system to identify opinions from text using rule-based approach, lexicon-based approach, or machine learning. In this paper, we propose and compare several deep learning models to solve sentiment analysis problem of the Internet Movie Database (IMDb) review sentiment dataset. The feature extractor consists of a convolutional layer, followed by a max pooling layer and a batch normalization layer. To solve the vanishing gradient problem, we use a residual connection to concatenate the input values with the extracted features before feeding the output into a recurrent layer. Our best model has an accuracy of 90.02%.

原文???core.languages.en_GB???
主出版物標題Proceedings - 2019 IEEE International Symposium on Multimedia, ISM 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁面335-338
頁數4
ISBN(電子)9781728156064
DOIs
出版狀態已出版 - 12月 2019
事件21st IEEE International Symposium on Multimedia, ISM 2019 - San Diego, United States
持續時間: 9 12月 201911 12月 2019

出版系列

名字Proceedings - 2019 IEEE International Symposium on Multimedia, ISM 2019

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???event.eventtypes.event.conference???21st IEEE International Symposium on Multimedia, ISM 2019
國家/地區United States
城市San Diego
期間9/12/1911/12/19

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