Application of Text Classification and Clustering of Twitter Data for Business Analytics.
In the recent years, social networks in business are gaining unprecedented popularity because oftheir potential for business growth. Companies can know more about consumers’ sentimentstowards their products and services, and use it to better understand the market and improve theirbrand. Thus, companies regularly reinvent their marketing strategies and campaigns to fitconsumers’ preferences. Social analysis harnesses and utilizes the vast volume of data in socialnetworks to mine critical data for strategic decision making. It uses machine learning techniques and tools in determining patterns and trends to gain actionable insights. This paper selected apopular food brand to evaluate a given stream of customer comments on Twitter. Several metricsin classification and clustering of data were used for analysis. A Twitter API is used to collecttwitter corpus and feed it to a Binary Tree classifier that will discover the polarity lexicon ofEnglish tweets, whether positive or negative. A k-means clustering technique is used to grouptogether similar words in tweets in order to discover certain business value. This paper attemptsto discuss the technical and business perspectives of text mining analysis of Twitter data andrecommends appropriate future opportunities in developing this emerging field.