Applying Naive Bayes Classifier Method for Sentiment Classification of Electric Cars

Abstract

In recent years, electric cars have become increasingly popular as an alternative to environmentally friendly vehicles in the automotive industry. These vehicles use electric power as an energy source that can mitigate the reliance on fossil fuels contribute to efforts to minimize greenhouse gas emissions and air pollution. However, the presence of electric cars raises pro and con opinions from the public. the conversation about electric cars has become one of the hot on social media. Twitter is a social media microblogging that permits its users to create short messages and share them easily and quickly. These opinions require sentiment analysis. The purpose of conducting sentiment analysis is to find out how people's perceptions and opinions on electric cars are leading in a favorable or unfavorable direction. Thus, sentiment analysis can help companies marketing strategies, and better business decisions. Then the opinions will be classified based on positive and negative categories. This investigation employs the naive classifier method to generate positive and negative sentiment towards electric cars on Twitter. The accuracy results of naive bayes obtained by using a confusion matrix in this research are 77.8%, with a dataset split composition of 70%:30%.