@inproceedings{aa48701a4572416985eeb35549506e27,
title = "Aspect-based Opinion Mining for Code-Mixed Restaurant Reviews in Indonesia",
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.",
keywords = "code-mixed, opinion mining, restaurant, stemming, stopwords",
author = "Andi Suciati and Indra Budi",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 23rd International Conference on Asian Language Processing, IALP 2019 ; Conference date: 15-11-2019 Through 17-11-2019",
year = "2019",
month = nov,
doi = "10.1109/IALP48816.2019.9037689",
language = "English",
series = "Proceedings of the 2019 International Conference on Asian Language Processing, IALP 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "59--64",
editor = "Man Lan and Yuanbin Wu and Minghui Dong and Yanfeng Lu and Yan Yang",
booktitle = "Proceedings of the 2019 International Conference on Asian Language Processing, IALP 2019",
address = "United States",
}