Twitter sentiment analysis: A case study in the automotive industry

Abstract

Sentiment analysis is one of the fastest growing areas which uses the natural language processing, text mining and computational linguistic to extract useful information to help in the decision making process. In the recent years, social media websites have been spreading widely, and their users are increasing rapidly. Automotive industry is one of the largest economic sectors in the world with more than 90 million cars and vehicles. Automotive industry is highly competitive and requires that sellers, automotive companies, carefully analyze and attend to consumers' opinions in order to achieve a competitive advantage in the market. Analysing consumers' opinions using social media data can be very great way for the automotive companies to enhance their marketing targets and objectives. In this paper, a sentiment analyses on a case study in the automotive industry is presented. Text mining and sentiment analysis are used to analyze unstructured tweets on Twitter to extract the polarity, and emotions classification towards the automotive classes such as Mercedes, Audi and BMW. We can note from the emotions classification results that, “joy” category is better for BMW comparing to Mercedes and Audi, The “sadness” percentage is larger for Audi and Mercedes comparing to BMW. Furthermore, we can note from the polarity classification that BMW has 72% positive tweets compared 79% for Mercedes and 83% for Audi. In addition, the results show that BMW has 8% negative polarity compared 18% for Mercedes and 16% for Audi.