Abstract

The era of information disclosure and social media tends to make people express their opinions on social media. Indonesia is one of the top five nations social media users in general, especially Twitter. This becomes an interesting thing when trying to see public opinion on a government service. Opinion mining can be used to get information from textual twitter to be processed into an information by classifying existing information into positive information classes and negative information classes. In this research, we try to do opinion mining on public opinion about Identification card (KTP) service in Surabaya city. We compare between supervised and unsupervised methods to see their performance for each classifier. In unsupervised the sentiwordnet approach is used to classify between negative and positive opinions. Supervised Support Vector Machine (SVM) method is used to create a classification model to define an opinion. Before the data is classified, preprocessing steps are used to make the data better. In addition, the Latent Dirichlet Allocation (LDA) approach is used to see for topics that tend to be strong which affects a negative or positive opinion. The result of the classification model by using SVM achieved accuracy rate of 75%.

Original languageEnglish
Title of host publicationProceedings - 2018 5th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2018
EditorsMochammad Facta, Munawar Agus Riyadi, M. Arfan, Eko Didik Widianto
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages125-130
Number of pages6
ISBN (Electronic)9781538655276
DOIs
Publication statusPublished - 13 Dec 2018
Event5th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2018 - Semarang, Indonesia
Duration: 26 Sept 201828 Sept 2018

Publication series

NameProceedings - 2018 5th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2018

Conference

Conference5th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2018
Country/TerritoryIndonesia
CitySemarang
Period26/09/1828/09/18

Keywords

  • big data analysis
  • lda
  • lexicon based
  • machine learning
  • opinion mining
  • sentiment analysis
  • text mining
  • topic modeling

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