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Volume 5 Issue 10

October 2019

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title

SURVEY OF ELECTION PREDICTION USING MACHINE LEARNING

Author(s):

Madhuri Nag

Keywords:

Sentiment Analysis; Twitter; Indian Elections; Naive Bayes; Support Vector Machine.

Abstract

Sentiment analysis is considered to be a category of machine learning and natural language processing. It is used to extricate, recognize, or portray opinions from different content structures, including news, audits and articles and categorizes them as positive, neutral and negative. It is difficult to predict election results from tweets in different Indian languages. We used Twitter Archiver tool to get tweets in Hindi language. We performed data (text) mining on election related tweets collected over a period that referenced five national political parties in India, during the campaigning period for general state elections in 2020. We made use of both supervised and unsupervised approaches. We utilized Dictionary Based, Naive Bayes, SVM algorithm and Random Forest Classifier to build our classifier and classified the test data as positive, negative and neutral. We identified the sentiment of Twitter users towards each of the considered Indian political parties. The results of the analysis were for the BJP (Bhartiya Janta Party), As it turned out, BJP will win in the 2020 general election, far more than any other political parties.

Other Details

Paper ID: IJSARTV
Published in: Volume : 5, Issue : 10
Publication Date: 10/2/2019

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