Call for Paper - November 2023 Edition
IJCA solicits original research papers for the November 2023 Edition. Last date of manuscript submission is October 20, 2023. Read More

Twitter Data Sentiment Analysis and Visualization

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
R. S. Gound, Priyanka V. Tikone, Shivani S. Suryawanshi, Dipanshu Nagpal

R S Gound, Priyanka V Tikone, Shivani S Suryawanshi and Dipanshu Nagpal. Twitter Data Sentiment Analysis and Visualization. International Journal of Computer Applications 180(20):14-16, February 2018. BibTeX

	author = {R. S. Gound and Priyanka V. Tikone and Shivani S. Suryawanshi and Dipanshu Nagpal},
	title = {Twitter Data Sentiment Analysis and Visualization},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2018},
	volume = {180},
	number = {20},
	month = {Feb},
	year = {2018},
	issn = {0975-8887},
	pages = {14-16},
	numpages = {3},
	url = {},
	doi = {10.5120/ijca2018916463},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Twitter is an online microblogging and social networking platform, which allows users to write short status, updates of maximum length 280 characters. These tweets reflect public sentiment about various topics and events happening. Analysing the public sentiment can help, firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange. Sentiment analysis techniques are widely popular for this purpose. In this paper, we have tried to define and compare various sentiment classification approaches/methods for finding out the sentiments behind the tweet.


  1. Anuja P Jain, Padma Dandannavar “Application of Machine Learning Techniques to Sentiment Analysis”, 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), July 2016.
  2. Walaa Medhat, Ahmed Hassan, “Sentiment analysis algorithms and applications:A survey” Shams Engineering Journal (2014) 5, 1093 – 1113.
  3. Bing Liu, “Sentiment Analysis and Opinion Mining”, Morgan and Claypool Publishers, May 2012.p.18-19,27-28,44-45,47,90-101.
  4. Andrei Sechelea, Tien Do Huu, Evangelos Zimos, and Nikos Deligiannis “Twitter Data Clustering and Visualization”, 23rd International Conference on Telecommunications (ICT).
  5. Martin Sarnovsky, Peter Butka, Andrea Huzvarova “Twitter data analysis and visualizations using the R language on top of the Hadoop platform”, IEEE 15th International Symposium on Applied Machine Intelligence and Informatics January 26-28, 2017.
  6. Rohit Joshi, Rajkumar Tekchandani, “Comparative Analysis of Twitter Data Using Supervised Classifiers”, proceedings of the 2016 International Conference on Inventive Computation Technologies (ICICT).
  7. Afroze Ibrahim Baqapuri, “Twitter Sentiment Analysis”,Department of Electrical Engineering, School of Electrical Engineering & Computer Science, National University of Sciences & Technology, Islamabad, Pakistan 2012.


Lexicon, Machine Learning, Natural Language Processing, Sentiment Analysis, Twitter