A Survey On: Sentiment Analysis framework of Twitter data Using Classification

  • Pratima Deshapnde
  • Purva Joshi
  • Pratiksha Pawar
  • Diptee Madekar
  • M.D. Salunke
Keywords: machine learning; sentiment analysis; Naïve Bayes; support vector machine; random forest.


 Sentiment means classify the opinions which are in the form of text. As we are classifying the text it must having different unstructured contains with information of particular subject. There are various social media sites which gives us information with their thoughts but twitter is biggest among them where anyone who having account on twitter can tweet their opinions of any subject in any certain or uncertain way. Hence, we get extra scope in mining of such data. The analysis is done with the help of machine learning algorithms on the dataset. Classification is done by the classifier algorithms. The reviews data is used for performing sentimental analysis. This paper gives idea about how the analysis is done on twitter data by using various algorithms and machine learning concepts. It is a survey of different papers for analyzing the sentiments of text. 


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How to Cite
Deshapnde, P., Joshi, P., Pawar, P., Madekar, D., & Salunke, M. (2020). A Survey On: Sentiment Analysis framework of Twitter data Using Classification. Asian Journal For Convergence In Technology (AJCT), 5(3), 118-122. Retrieved from http://asianssr.org/index.php/ajct/article/view/935

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