Application of Text Classification and Clustering of Twitter Data for Business Analytics - 2018


In the recent years, social networks in business are gaining unprecedented popularity as a result of of their potential for business growth. Firms can recognize more about shoppers' sentiments towards their product and services, and use it to better perceive the market and improve their whole. Thus, firms often reinvent their selling strategies and campaigns to suit customers' preferences. Social analysis harnesses and utilizes the vast volume of data in social networks to mine essential knowledge for strategic call making. It uses machine learning techniques and tools in determining patterns and trends to realize actionable insights. This Project selected a popular food brand to judge a given stream of client comments on Twitter. Several metrics in classification and clustering of knowledge were used for analysis. A Twitter API is used to gather twitter corpus and feed it to a Binary Tree classifier that can discover the polarity lexicon of English tweets, whether or not positive or negative. A k-means clustering technique is used to group together similar words in tweets so as to find bound business price. This Project attempts to debate the technical and business perspectives of text mining analysis of Twitter data and recommends applicable future opportunities in developing this emerging field.

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