Machine learning based twitter data sentiment classification on real time ‘clean India mission’ tweets

 
 
 
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  • Abstract


    Social networking sites are popular medium to share opinion on various topics. Twitter is one of the social networking site used for tweet posting. Sentiment classification deal with finding the polarity of these tweets as positive, negative or neutral. This analysis can be useful in decision support in different ways. The aim of this paper is to discuss various machine learning algorithm for twitter sentiment classification, compare their results on the basis of Accuracy, Precision, Recall and F-Score. A real time data set for training and testing is created after extraction and cleaning of tweets on “Clean India Mission or Swachh Bharat Abhiyan”. “Clean India Mission” is a campaign by government of India to clean the country. This paper also compares machine learning algorithms with Bagging, Boosting and Random Forest ensemble approaches.

     


  • Keywords


    SVM; Maxent; RF; Naïve Bayes; Clean India Mission; Swachh Bharat Abhiyan.

  • References


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Article ID: 19275
 
DOI: 10.14419/ijet.v7i4.19275




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