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An Empirical Evaluation of Correlation Based Feature Selection for Tweet Sentiment Classification

Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 643)

Abstract

This paper presents a study on Twitter sentiment analysis where tweets are gathered and sentiments behind the tweet are evaluated by using various machine learning techniques. It presents an empirical evaluation of correlation based feature selection for sentiment classification on twitter data. The data is extracted from twitter in real time and text preprocessing and feature extraction is applied on the textual data. Correlation based attribute selection methods are used and machine learning classifiers (SVM, Naïve Bayes, Random Forest, Meta classifier, SGD, Logistic Regression) are compared on various performance parameters to show which classifier gives better results. The results show that when STWV with Attribute Selection methods are used together in the same setup, the classifiers give accuracy between 78 and 88% with about 0.88 true positive rate and 0.15 false positive rate which is far better when no attribute selection method is used.

Keywords

Twitter analysis Sentiment analysis Correlation based feature selection Machine learning techniques 

Notes

Acknowledgements

This publication is an outcome of the R&D work undertaken project under the Visvesvaraya Ph.D. Scheme of Ministry of Electronics and Information Technology, Government of India, being implemented by Digital India Corporation and with the cooperation of GGSIP University.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.USICT, Guru Gobind Singh Indraprastha UniversityDelhiIndia

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