Analysis on Opinion Mining Using Combining Lexicon-Based Method and Multinomial Naïve Bayes

DOI

10.2991/icoiese-18.2019.38How to use a DOI?

Keywords

opinion mining, multinomial naive bayes, lexicon-based method

Abstract

Opinion mining is the analysis of the opinion by looking at the sentiment, behavior, or emotions contained in a product. Some of the opinion mining methods are using the lexicon-based and supervised learning. Lexicon-based method has a low recall, while supervised learning has good accuracy but requires a long training period. Therefore on this paper will be discussed to combine lexicon-based method with one of the supervised learning method, namely Multinomial Naïve Bayes for English language and classify opinion based on the sentiment class, ie positive and negative. Feature extraction that used in this research are unigram, POS-Tagging, and score-based feature on lexicon. The output of the system is the polarity of each document and the performance will be calculated using Precision, Recall, and F-measure. With the implementation of opinion mining with combining lexicon-based method and Multinomial Naive Bayes, this research get accuracy 0.637.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Geriska Isabelle
AU  - Warih Maharani
AU  - Ibnu Asror
PY  - 2019/03
DA  - 2019/03
TI  - Analysis on Opinion Mining Using Combining Lexicon-Based Method and Multinomial Naïve Bayes
BT  - Proceedings of the 2018 International Conference on Industrial Enterprise and System Engineering (IcoIESE 2018)
PB  - Atlantis Press
SP  - 214
EP  - 219
SN  - 2589-4943
UR  - https://doi.org/10.2991/icoiese-18.2019.38
DO  - 10.2991/icoiese-18.2019.38
ID  - Isabelle2019/03
ER  -