Application of Text Classification and Clustering of Twitter Data || JAVA || Mysql || Apache Tomcat
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ABSTRACT: In the recent years, social networks in business are
gaining unprecedented popularity because of their potential for
business growth. Companies can know more about consumers’
sentiments towards their products and services, and use it to
better understand the market and improve their brand. Thus,
companies regularly reinvent their marketing strategies and
campaigns to fit consumers’ preferences. Social analysis
harnesses and utilizes the vast volume of data in social networks
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 a popular food brand to evaluate a given
stream of customer comments on Twitter. Several metrics in
classification and clustering of data were used for analysis. A
Twitter API is used to collect twitter corpus and feed it to a
Binary Tree classifier that will discover the polarity lexicon of
English tweets, whether positive or negative. A k-means
clustering technique is used to group together similar words in
tweets in order to discover certain business value. This paper
attempts to discuss the technical and business perspectives of text
mining analysis of Twitter data and recommends appropriate
future opportunities in developing this emerging field.