Sentiment Analysis on Microblog Content

Authors

  • YK Pitale Computer, St.John College Of Engineering And Management, Mumbai University, Palghar, India
  • DC Salot Computer, St.John College Of Engineering And Management, Mumbai University, Palghar, India
  • RS Mhatre Computer, St.John College Of Engineering And Management, Mumbai University, Palghar, India
  • T D’abreo Computer, St.John College Of Engineering And Management, Mumbai University, Palghar, India

DOI:

https://doi.org/10.26438/ijcse/v6i3.333336

Keywords:

Opinion mining, sentiment analysis, twitter, natural language processing, sentimento

Abstract

Due to rapid evolution of micro blog content on social media websites, internet has become a vital medium for a huge source of data. Internet has change the general perspective of socializing and finding the information regarding various (entities). Use of data from social networks for different purposes, such as election prediction, sentimental analysis, marketing, communication, business, and education, is increasing day by day. Due to overwhelming amount of user opinion, reviews, and suggestions available through the web platform, and it helps in analysing and taking better decisions. Micro blogging websites becomes a major source for the gauging the perspective of the user. In this paper, we are using the concept of opinion mining and analysing tweets to classify the data and extract the sentiments from it. Extraction of valuable information precisely from social media website and thus Several decisions can be made more efficiently using sentiments of individuals. Verified reviews need to be used for better accuracy. Proposed system is tested on the collection of real time data extracted from Twitter. The resultant opinion is represented in the form of graph and sentimento.

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Published

2025-11-12
CITATION
DOI: 10.26438/ijcse/v6i3.333336
Published: 2025-11-12

How to Cite

[1]
Y. Pitale, D. Salot, R. Mhatre, and T. D’abreo, “Sentiment Analysis on Microblog Content”, Int. J. Comp. Sci. Eng., vol. 6, no. 3, pp. 333–336, Nov. 2025.

Issue

Vol. 6 No. 3 (2018): IJCSE March Edition

Section

Review Article

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