Sentiment Analysis of IDAHOBIT Celebrations using Naïve Bayes and Decision Tree Algorithms

Abstract

The development of LGBTIQ in Indonesia reflects the shift in culture and the emergence of this phenomenon has attracted the attention of the Indonesian people. The use of NLP, ML, and statistics technology in tweet analysis can be used to identify sentiments contained in tweets. This study compares Naïve Bayes algorithm and Decision Tree in sentiment analysis classification, in which the multilingual sentiment analysis method is used in the labeling process of training data. Naïve Bayes results give the best classification with 100% accuracy, precision, and recall, and the number of positive sentiments is 385, negative sentiments are 3117, and neutral sentiments are 899. It looks that the negative class is the most superior compared to other classes. This proves that the Indonesian people have an unfavorable response to the IDAHOBIT celebration.