SENTIMENT ANALYSIS OF INDONESIA’S DIGITAL WALLET USING COMBINATION MACHINE LEARNING AND EMOTICON WEIGHT

Abstract

Opinions on social media can be used to determine user sentiment by using sentiment analysis concept. Sentiment analysis requires several important stages, namely, preprocessing, feature extraction and classification method stages. The preprocessing stage was carried out to eliminate inconsistent data. In previous research, punctuation marks removal was applied at the preprocessing stage which can eliminate the emoticon position. Emoticons are a combination of punctuation marks. According to previous research, the emoticon feature has no contribution in sentiment analysis. There is another suggestion to maintain an emoticon position like converting an emoticon into a more relevant word such as :) into a “smile”. However, the feature of emoticon weights has not been considered in the sentiment analysis process. In order to consider the role of emoticons and to improve sentiment analysis performance, we propose using a combination of machine learning and emoticon weights. We perform emoticon weight based on probability and sentiment score. Each probability value and sentiment score of the emoticon will be normalized using the z-score method. There are several machine learning methods that have the best classification success rates, namely, Naïve Bayes and SVM. Based on the evaluation results of the proposed model, the best accuracy is 87% - 89% when using the combination of machine learning and emoticon sentiment score. Based on the results also show that the emoticon sentiment score has a significant effect on the accuracy of sentiment analysis.