Sentiment Analysis using Sentiwordnet and Machine Learning Approach (Indonesia general election opinion from the twitter content)

The computational process of identifying and categorizing opinions that are expressed in the piece of text could be employed to determine information insight and the writer's opinion toward a particular topic. Most sentiment analysis employed for English text. Contrarily, a plethora method for sentiment analysis has been reported that the task stayed an interesting question for Indonesian text. The invention of machine learning models and broad accessibility of Twitter data on previous years have derived many researchers to take a machine learning model to resolve the sentiment analysis problem. The objective of this study is to build a sentiment analysis model using Sentiwordnet and machine learning for Indonesia general election opinion in Indonesian text from the twitter content. The data of the tweet was taken namely, the username, and the tweet itself. The theme of the tweet was the topic related to the 2019 general election figures, namely Joko Widodo and Prabowo Subianto. The period of data collection was November 13, 2018, to January 11, 2019, during the campaign period. The tweet was in Indonesia language. The result revealed sentiment analysis with the Naive Bayes classification method showed 74.94% accuracy for Joko Widodo topic, while 71.37% accuracy for Prabowo topic.