Twitter Sentiment Analysis: A Case Study for Apparel Brands

Abstract

Social media especially Twitter is providing a space for expression and opinions, where users discuss various events, services, and brands. Entrepreneurs are in continuous need of the feedback about their services to improve the quality and quantity. However, due to the bulk amount of data, it's difficult to detect the consumer's opinions. This article deliberates the problems about the Twitter data for the sentiment analysis. Furthermore, it implements the text mining and document-based sentiment on the preprocessed Twitter data through the machine learning techniques, Naïve Bayes and lexicon dictionary. Our case study is to find the public opinion about the top two apparel international brands and compare the positive and negative attitude of common users about each brand. We found that positive reviews of Adidas are more than the Nike while there is the slight difference in negative reviews. It founds that people want to discuss the other brands as comparisons when they are talking about just one brand.