Sentiment Analysis of Electric Cars Using Naive Bayes Classifier Method

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 reduce dependence on fossil fuels so as to contribute to efforts to reduce greenhouse gas emissions and air pollution. However, the presence of electric cars raises pro and con opinions from the public. Where, the conversation about electric cars has become one of the hot conversations on social media twitter. Twitter is a microblogging-based social media that facilitates its users to write 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 positive direction or in a negative direction. Thus, sentiment analysis can help companies in designing marketing strategies, product development, and making better business decisions. Then the opinions will be classified based on positive and negative categories. This research uses the naive bayes 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 78.57% with a dataset split composition of 80%:20%.