Data Mining Approach of Text Classification and Clustering of Twitter Data for Business Analytics

Authors

  • P. Meghanasree  PG Scholar, Department of Computer Science and Engineering, Sri Mittapalli College of Engineering, U9, Thummalapalem, Prathipadu, Guntur, Andhra Pradesh, India
  • Dr. S. Gopi Krishna  Professor, Department of Computer Science and Engineering, Sri Mittapalli College of Engineering, U9, Thummalapalem, Prathipadu, Guntur, Andhra Pradesh, India

DOI:

https://doi.org//10.32628/CSEIT195429

Keywords:

Sentiment Analysis, Classification, Clustering, Twitter, Data Mining

Abstract

The increasing popularity of micro-blogging sites like Twitter, which facilitates users to exchange short messages (tweets) is an impetus for data analytics tasks for business development. Twitter has a huge amount of data. Twitter’s API allows you to do complex queries like pulling every tweet about a certain topic. So, Companies can know more about consumers’ sentiments towards their products and services and use them to better understand the market and improve their brand. In 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 classifier algorithm that will discover the polarity lexicon of English tweets, whether positive or negative. A clustering technique is used to group together similar words in tweets in order to discover certain business value.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-07-30

Issue

Volume 5, Issue 4, July-August-2019

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
P. Meghanasree, Dr. S. Gopi Krishna, " Data Mining Approach of Text Classification and Clustering of Twitter Data for Business Analytics, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 4, pp.138-142, July-August-2019. Available at doi : https://doi.org/10.32628/CSEIT195429