A Hybrid Model for Social Media Sentiment Analysis for Indonesian Text

Abstract

Sentiment analysis for Indonesian social media text is very important because the text content in social media is very diverse and requires an accurate method that can produce an analysis describing the state of the actual data. The main problem in sentiment analysis for Indonesian text on social media is unstructured text data and the use of non-standard languages such that sentiment analysis often produces errors. This paper focuses on sentiment analysis using a hybrid model that combines lexicon based and maximum entropy methods to classify the sentiments of Indonesian public opinion on government. The method consists of extracting datasets, preprocessing, lexicon-based classification, machine learning training, machine learning classification, and result interpretation. The results of the study produce 91 classifications of neutral sentiment, 51 document negative sentiments, 39 document positive sentiments and 152 document of mix sentiments. Based on the evaluation results, the hybrid sentiment model for Indonesian Language sentiment analysis on social media produced a pretty good accuracy score of 84.31% compared to previous studies. The implication of this study is to produce a sentiment analysis system with a hybrid method for Indonesian text on social media.