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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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%.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Symposium on Multimedia, ISM 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages335-338
Number of pages4
ISBN (Electronic)9781728156064
DOIs
StatePublished - Dec 2019
Event21st IEEE International Symposium on Multimedia, ISM 2019 - San Diego, United States
Duration: 9 Dec 201911 Dec 2019

Publication series

NameProceedings - 2019 IEEE International Symposium on Multimedia, ISM 2019

Conference

Conference21st IEEE International Symposium on Multimedia, ISM 2019
Country/TerritoryUnited States
CitySan Diego
Period9/12/1911/12/19

Keywords

  • convolutional neural network
  • neural network
  • recurrent neural network
  • sentiment analysis

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