Twitter Opinion Mining Analysis of Web-Based Handphone Brand Using Naïve Bayes Classification Method

Abstract

Social Media is now very commonly used for the benefit of society. People mostly use social media to convey information, give opinions, even for media to express themselves. One of the social media that is widely used to convey this information is Twitter. From the use of Twitter, a public opinion tweet emerged about a mobile phone product. The more that is posted on Twitter about cellphones, the more public opinion will arise about cellphone brands. From these opinions, a classification is needed that can distinguish Neutral, Negative, or Positive Opinions. Sentiment analysis or opinion mining is one part of text mining that can help with these problems. In connection with the above, an application is designed that can analyze sentiment analysis from Twitter using the Naïve Bayes classification method. The results of the application of the Naïve Bayes classification method will result in a classification of sentiments into neutral, negative, or positive opinions