Evaluation of the accuracy of transfer learning on sentiment analysis for Indonesian tweets

Kartika Syskya Wydya, Hendri Murfi, Yudi Satria

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

1 Citation (Scopus)

Abstract

Sentiment analysis is an automatic process of understanding, extracting and processing textual data to obtain the sentiment information. From machine learning point of view, the sentiment analysis is a supervised learning problem whose training and predicting data come from a similar domain. When domain changes, the machine learning model must be rebuilt from scratch using new training data. New training data requires manual labeling process which is very costly and time-consuming. Therefore, it would be more effective and efficient using transfer learning which uses the training data from an already available domain to deal with the estimating data on different domains. In this paper, we evaluate the accuracy of the transfer learning on sentiment analysis for Indonesian tweets. Our simulations show that the accuracy of the transfer learning is still lower than that of the supervised learning. Moreover, the bi-gram features can improve the accuracy of the transfer learning.

Original languageEnglish
Title of host publicationProceedings - 2017 1st International Conference on Informatics and Computational Sciences, ICICoS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages231-236
Number of pages6
ISBN (Electronic)9781538609033
DOIs
Publication statusPublished - 30 Jan 2018
Event1st International Conference on Informatics and Computational Sciences, ICICoS 2017 - Semarang, Indonesia
Duration: 15 Nov 201716 Nov 2017

Publication series

NameProceedings - 2017 1st International Conference on Informatics and Computational Sciences, ICICoS 2017
Volume2018-January

Conference

Conference1st International Conference on Informatics and Computational Sciences, ICICoS 2017
Country/TerritoryIndonesia
CitySemarang
Period15/11/1716/11/17

Keywords

  • Sentiment Analysis
  • Support Vector Machine
  • Transfer Learning
  • Twitter

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