Mobile application review classification for the Indonesian language using machine learning approach

Yudo Ekanata, Indra Budi

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

5 Citations (Scopus)

Abstract

The number of user reviews for a mobile app can reach thousands so it will take a lot of time for app developers to sort through and find information that is important for further app development. Therefore, this study aims to automatically classify mobile application user reviews. Automatic classification conducted in this study is using machine learning approach. The features extracted from user review are unigram, bigram, star rating, review length, as well as the ratio of the number of words with positive and negative sentiment. For classification algorithms, we used Naïve Bayes, Support Vector Machine, Logistic Regression and Decision Tree. The experiment result shows that Logistic Regression gives the best F-Measure of 85% when combined with unigram plus sentence length and sentiment score. Unigram was proven as the most important feature since the additional features like sentence length and sentiment score only increased the F-measure around 1%. Bigram and star rating has negative impact on the classifier performance.

Original languageEnglish
Title of host publication2018 4th International Conference on Computer and Technology Applications, ICCTA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages117-121
Number of pages5
ISBN (Electronic)9781538669952
DOIs
Publication statusPublished - 27 Jun 2018
Event4th International Conference on Computer and Technology Applications, ICCTA 2018 - Istanbul, Turkey
Duration: 3 May 20185 May 2018

Publication series

Name2018 4th International Conference on Computer and Technology Applications, ICCTA 2018

Conference

Conference4th International Conference on Computer and Technology Applications, ICCTA 2018
CountryTurkey
CityIstanbul
Period3/05/185/05/18

Keywords

  • app review
  • classification
  • machine learning
  • text mining

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