Decision Making Framework Based On Sentiment Analysis in Twitter Using SAW and Machine Learning Approach

Abstract

One type of social media that is often used is Twitter. The development of social media is so fast that even users reach 326 million and produce 500 million tweets every day in July 2018. Users can send, change, and read short messages which have been called tweets. Tweets can contain facts or opinions so it is very beneficial to be analyzed. The results of this analysis can be in the form of stock market predictions, elections, reaction events or news and measuring subjectivity. The activity of analyzing tweet is a series of sentiment analysis activities. However, the results of sentiment analysis are cumulative in percentage of tweet polarity and only provide a overview for decision making. So, the intuitive aspect still plays a role in deciding the results of the Sentiment Analysis. Therefore, there is a need for more specific modeling of sentiment analysis results. In the decision making phase, the results of the Sentiment Analysis are still in the Intelligence phase or can be called the Problem Discovery. To proceed again to the Design phase until with a Choice, it is necessary to have a Decision Support System (DSS). In this study trying to propose a decision making framework based on the results of sentiment analysis from the Tweet dataset. Sentiment analysis is built using the machine learning approach. Furthermore, the results of this study indicate that the SAW method can accept the input polarity of the number of tweets and produce alternative decision weights.