Performance comparison of text-based sentiment analysis using recurrent neural network and convolutional neural network

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

One biggest challenge in sentiment analysis is that it should include Natural Language Processing (NLP), to make the machine understand the human language. With the current development of Artificial Neural Network (ANN), with its implementation, computer can learn to understand human language by such learning mechanism There are many types of ANN and for this research Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) were used and compared on their performance. The text data for the sentiment analysis was taken from Stanford publication and transformation from text to vectors were conducted using word2vec. The result shows that RNN is better than CNN. Even the difference of accuracy is not significant with 88.35% ±0.07 for RNN and 87.11% ±0.50 for CNN, the training time for RNN only need 8.256 seconds while CNN need 544.366 seconds.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Conference on Communication and Information Processing, ICCIP 2017
PublisherAssociation for Computing Machinery
Pages19-23
Number of pages5
ISBN (Electronic)9781450353656
DOIs
Publication statusPublished - 24 Nov 2017
Event3rd International Conference on Communication and Information Processing, ICCIP 2017 - Tokyo, Japan
Duration: 24 Nov 201726 Nov 2017

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Communication and Information Processing, ICCIP 2017
CountryJapan
CityTokyo
Period24/11/1726/11/17

Keywords

  • Convolutional Neural Network
  • Natural Language Processing
  • Recurrent Neural Network
  • Sentiment analysis
  • Word2vec

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