Sentiment Analysis on Automobile Brands Using Twitter Data

Abstract

User generated contents in a very big number is freely available on different social media sites now a day. Companies to increase their competitive advantages keep an eye on their competing companies and closely analyze the data that are generated by their customers on their social media sites. Analysis of sentiments is the quickest growing field that utilizes text mining, computational linguistics and natural language processing, linguistic mining of text and calculation to extricate valuable data to assist in decision making. The automobiles business is extremely competing and needs that supplier, automobile corporations, carefully analyze and address the views of consumers with a specific end goal to accomplish an upper hand in the market. It is a great way to analyze the views of consumers through the data of social media sites; what’s more, it is also helpful for automobiles companies to improve their goals and objectives of marketing. In this research, presents an analysis of sentiment on a case study of automobiles industry. Sentiment analysis and text mining are utilized to analyze and break down unstructured Twitter’s tweets to take out automobile classes’ polarity for example, Honda, Toyota, BMW, Audi, and Mercedes. According to the classification of the polarity, you notice that Audi has 87% of the positive tweets compared to 74% for BMW, 84% for Honda, 70% for Toyota and 81% for Mercedes. What’s more, the results demonstrate that Audi has negative polarity 18% against 10% for BMW, 20% for Mercedes, 15% for Honda and 25% for Toyota.