Sentiment Analysis of Twitter Data: A Hybrid Approach

Ankit Srivastava, Vijendra Singh and Gurdeep Singh Drall
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Ankit Srivastava: The NorthCap University, Gurgaon, India
Vijendra Singh: The NorthCap University, Gurgaon, India
Gurdeep Singh Drall: The NorthCap University, Gurgaon, India

International Journal of Healthcare Information Systems and Informatics (IJHISI), 2019, vol. 14, issue 2, 1-16

Abstract: Over the past few years, the novel appeal and increasing popularity of social networks as a medium for users to express their opinions and views have created an accumulation of a massive amount of data. This evolving mountain of data is commonly termed Big Data. Accordingly, one area in which the application of new techniques in data mining research has significant potential to achieve more precise classification of hidden knowledge in Big Data is sentiment analysis (aka optimal mining). A hybrid approach using Naïve Bayes and Random Forest on mining Twitter datasets is presented here as an extension of previous work. Briefly, relevant data sets are collected from Twitter using Twitter API; then, use of the hybrid methodology is illustrated and evaluated against one with only Naïve Bayes classifier. Results show better accuracy and efficiency in the sentiment classification for the hybrid approach.

Date: 2019
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