Analysis of Convolutional Neural Network for Lifelong Learning on Indonesian Sentiment Analysis

Zaid Abdurrahman, Hendri Murfi, Yekti Widyaningsih

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

Abstract

Sentiment analysis is a process to obtain the tendency of the authors in an article. Sentiment analysis classifies textual data into a class of positive, negative, or neutral sentiments. CNN is one of the deep learning algorithms capable of classifying textual data into positive, negative, or natural classes. In general, the standard learning methods learn from one domain to produce a model. Another learning paradigm is lifelong learning which is believed to be able to accumulate learning from various domains for learning in the new domain. In this paper, we examine lifelong learning of CNN for sentiment analysis on Indonesian textual data. Our simulation shows that the accuracy of CNN increases with the increase in the number of source domains where CNN learns. This shows that lifelong learning using CNN works well for sentiment analysis on Indonesian textual data.

Original languageEnglish
Title of host publicationICICSE and ICACTE 2020 - Proceedings of 2020 International Conference on Internet Computing for Science and Engineering - 2020 the 13th International Conference on Advanced Computer Theory and Engineering
PublisherAssociation for Computing Machinery
Pages64-69
Number of pages6
ISBN (Electronic)9781450377348
DOIs
Publication statusPublished - 14 Jan 2020
Event2020 International Conference on Internet Computing for Science and Engineering, ICICSE 2020 and the 13th International Conference on Advanced Computer Theory and Engineering, ICACTE 2020 - Virtual, Online, Maldives
Duration: 18 Sep 202020 Sep 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2020 International Conference on Internet Computing for Science and Engineering, ICICSE 2020 and the 13th International Conference on Advanced Computer Theory and Engineering, ICACTE 2020
Country/TerritoryMaldives
CityVirtual, Online
Period18/09/2020/09/20

Keywords

  • Convolutional Neural Network
  • Lifelong Learning
  • Machine Learning
  • Sentiment Analysis

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