Aspect-based Opinion Mining for Code-Mixed Restaurant Reviews in Indonesia

Andi Suciati, Indra Budi

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

4 Citations (Scopus)

Abstract

The goal of opinion mining is to extract the sentiment, emotions, or judgement of reviews and classified it. These reviews are very important because they can affect the decision-making from a person. In this paper, we conducted an aspect-based opinion mining research using customer reviews of restaurants in Indonesia and we focused into analyzing the code-mixed dataset. The evaluation conducted by making four scenarios namely removing stopwords without stemming, without removing stopwords but with stemming, without removing stopwords and stemming, and preprocessing with removing stopwords and stemming. We compared five algorithms which are Random Forest (RF), Multinomial Naive Bayes (NB), Logistic Regression (LR), Decision Tree (DT), and Extra Tree classifier (ET). The models were evaluated by using 10 folds cross validation, and the results show that all aspects achieved highest scores with different algorithms. LR achieved highest score for food (81.76%) and ambience (77.29%) aspects while the highest score for price (78.71%) and service (85.07%) aspects were obtained by DT.

Original languageEnglish
Title of host publicationProceedings of the 2019 International Conference on Asian Language Processing, IALP 2019
EditorsMan Lan, Yuanbin Wu, Minghui Dong, Yanfeng Lu, Yan Yang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages59-64
Number of pages6
ISBN (Electronic)9781728150147
DOIs
Publication statusPublished - Nov 2019
Event23rd International Conference on Asian Language Processing, IALP 2019 - Shanghai, China
Duration: 15 Nov 201917 Nov 2019

Publication series

NameProceedings of the 2019 International Conference on Asian Language Processing, IALP 2019

Conference

Conference23rd International Conference on Asian Language Processing, IALP 2019
CountryChina
CityShanghai
Period15/11/1917/11/19

Keywords

  • code-mixed
  • opinion mining
  • restaurant
  • stemming
  • stopwords

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