Application of text classification and clustering of Twitter data for business analytics

Halibas, A, Shaffi, A and Mohamed, M 2018, 'Application of text classification and clustering of Twitter data for business analytics', in Proceedings of Majan International Conference (MIC 2018), Muscat, Oman, 19-20 March 2018, pp. 1-7.


Document type: Conference Paper
Collection: Conference Papers

Title Application of text classification and clustering of Twitter data for business analytics
Author(s) Halibas, A
Shaffi, A
Mohamed, M
Year 2018
Conference name MIC 2018
Conference location Muscat, Oman
Conference dates 19-20 March 2018
Proceedings title Proceedings of Majan International Conference (MIC 2018)
Publisher IEEE
Place of publication Piscataway, United States
Start page 1
End page 7
Total pages 7
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.
Subjects Knowledge Representation and Machine Learning
Business Information Management (incl. Records, Knowledge and Information Management, and Intelligence)
Keyword(s) Twitter
Sentiment Analysis
Decision Tree
k-means
Social Media
DOI - identifier 10.1109/MINTC.2018.8363162
Copyright notice © 2018 IEEE
ISBN 9781538637616
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