TWEEZER – Tweets Analysis

Authors

  • Anannya Gupta B.Tech Student, Department of I.T., MIET, Meerut, INDIA
  • Deepali B.Tech Student, Department of I.T., MIET, Meerut, INDIA
  • Manvesh Ahlawat B.Tech Student, Department of I.T., MIET, Meerut, INDIA
  • Vedant B.Tech Student, Department of I.T., MIET, Meerut, INDIA
  • Swati Sharma Assistant Professor, Department of I.T., MIET, Meerut, INDIA

DOI:

https://doi.org/10.31033/ijemr.10.2.12

Keywords:

Sentiment Analysis, Naïve Bayes, Hashtag Classification, Classification Technique

Abstract

Twitter is one in all the foremost used applications by the people to precise their opinion and show their sentiments towards different occasions. Sentiment analysis is an approach to retrieve the sentiment through the tweets of the general public. Twitter sentiment analysis is application for sentiment analysis of information which are extracted from the twitter(tweets). With the assistance of twitter people get opinion about several things round the nation .Twitter is one such online social networking website where people post their views regarding to trending topics .It s huge platform having over 317 million users registered from everywhere the globe. a decent sentimental analysis of information of this huge platform can result in achieve many new applications like – Movie reviews, Product reviews, Spam detection, Knowing consumer needs, etc. during this paper, we used two specific algorithm –Naïve Bayes Classifier Algorithm for polarity Classification & Hashtag classification for top modeling. this system individually has some limitations for Sentiment analysis. The goal of this report is to relinquish an introduction to the present fascinating problem and to present a framework which is able to perform sentiment analysis on online mobile reviews by associating modified naïve bayes means algorithm with Naïve bayes classification.

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Published

2020-04-30

How to Cite

Anannya Gupta, Deepali, Manvesh Ahlawat, Vedant, & Swati Sharma. (2020). TWEEZER – Tweets Analysis. International Journal of Engineering and Management Research, 10(2), 111–115. https://doi.org/10.31033/ijemr.10.2.12