Analysis on Opinion Mining Using Combining Lexicon-Based Method and Multinomial Naïve Bayes
Geriska Isabelle, Warih Maharani, Ibnu Asror
Available Online March 2019.
- https://doi.org/10.2991/icoiese-18.2019.38How to use a DOI?
- opinion mining, multinomial naive bayes, lexicon-based method
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - https://doi.org/10.2991/icoiese-18.2019.38 ID - Isabelle2019/03 ER -