Sentiment Analysis of English Tweets Using Data Mining

Authors

  • Kaur A Dept. of Computer Engineering Yadavindra College of Engineering, Punjabi University Guru Kashi Campus, Talwandi Sabo, Bathinda, Punjab, India
  • Baghla S Dept. of Computer Engineering Yadavindra College of Engineering, Punjabi University Guru Kashi Campus, Talwandi Sabo, Bathinda, Punjab, India

DOI:

https://doi.org/10.26438/ijcse/v6i10.276284

Keywords:

Data Mining, Sentiment Analysis, Twitter, Classification

Abstract

Social media has been used for expressing and sharing the thoughts of people with different events. Sentiment analysis is being used for computing and satisfying a view of a person given in a piece of a text, to identify persons thinking about any topic is positive, negative or neutral. In the present work sentiment analysis has been used to analyze people’s sentiments, opinions, and emotions towards entities. In this work, sentiment 140 tools have been used for the collection of tweets on different topics. The collected tweets have been preprocessed. Different techniques have been used to present work. Classification technique has been used for the analysis of tweets how many positive, negative and neutral tweets. Sentiment analysis algorithm has been used to analyze tweets whether tweets are positive, negative or neutral. An autocorrect option has been also used to correct the sentence. Sentiment analysis has been used parameters such as accuracy, predictive and automation

References

[1] R. Mehta, D.S. Jain, “Sentiment Mining and Related Classifiers: A Review”, IOSR Journal of Computer Engineering, Vol.18, Issue.1, pp.50-54, 2016.

[2] Chandni, N. Chndra, S. Gupta, R. Pahade, “Sentiment Analysis and its Challenges”, International Journal of Engineering Research & Technology, Vol.4, Issue.3, pp.968-970, 2015.

[3] A.P. Rajan, S.P. Victor, “Web Sentiment Analysis for Scoring Positive or Negative Words Using Tweeter Data”, International Journal of Computer Applications, Vol.96, Issue.6, pp.33-37, 2014.

[4] A. Gupta, J. Pruthi, N. Sahu, “Sentiment Analysis of Tweets Using Machine Learning Approach”, International Journal of Computer Science and Mobile Computing, Vol.6, Issue.4, pp.444-458, 2017.

[5] B. Alvares, N. Thakur, S. Patil, D. Fernandes, K. Jain, “Sentiment Analysis Using Opinion Mining”, International Journal of Engineering Research & Technology, Vol.5, Issue.4, pp.88-91, 2016.

[6] B.M. Bandgar, D.S. Sheeja, “Analysis of Real Time Social Tweets for Opinion Mining”, International Journal of Applied Engineering Research, Vol.11, Issue.2, pp.1404-1407, 2016.

[7] B.S. Dattu, P. Deipali, V.Gore, “A Survey on Sentiment Analysis on Twitter Data Using Different Techniques”, International Journal of Computer Science and Technologies, Vol.6, Issue.6, pp.5358-5362, 2015.

[8] D.E. Oleary, “Twitter Mining for Discovery, Prediction and Causality: Applications and Methodologies”, International Journal of Intelligent Systems in Accounting and Finance Management, Vol.22, Issue.3, pp.222-247, 2015.

[9] G. Sabarmathi, D.R. Chinnaiyan, “Reliable Data Mining Tasks and Techniques for Industrial Applications”, IAETSD Journal for Advanced Research in Applied Sciences, Vol.4, Issue.7, pp.138-142, 2017.

[10] H.P. Rahmath, “Opinion Mining and Sentiment Analysis- Challenges and Applications”, International Journal of Application or Innovation in Engineering & Management, Vol.3, Issue.5, pp.401-403, 2014.

[11] I. Smeureanu, C. Bucur, “Applying Supervised Opinion Mining Techniques on Online User Reviews”, Informatica Economică, Vol.16, Issue.2, pp.81-91, 2012.

[12] K.I. Umar, F. Chiroma, “Data Mining for Social Media Analysis: Using Twitter to Predict the 2016 US Presidential Election”, International Journal of Scientific & Engineering Research, Vol.7, Issue.10, pp.1972-1980, 2016.

[13] K. Sutar, S. Kasab, S. Kindare, P. Dhule, “Sentiment Analysis: Opinion Mining of Positive, Negative or Neutral Twitter Data Using Hadoop”, International Journal of Computer Science and Network, Vol.5, Issue.1, pp. 177-180, 2016.

[14] L.J. Sheela, “A Review of Sentiment Analysis in Twitter Data Using Hadoop”, International Journal of Database Theory and Application, Vol.9, Issue.1, pp.77-86, 2016.

[15] S.A.A. Hridoy, M.T. Ekram, M.S. Islam, F. Ahemed, R.M. Rahman, “Localized Twitter Opinion Mining Using Sentiment Analysis”, Decision Analytics, Vol.2, Issue.1, pp.1-19, 2015.

Downloads

Published

2025-11-17
CITATION
DOI: 10.26438/ijcse/v6i10.276284
Published: 2025-11-17

How to Cite

[1]
A. Kaur and S. Baghla, “Sentiment Analysis of English Tweets Using Data Mining”, Int. J. Comp. Sci. Eng., vol. 6, no. 10, pp. 276–284, Nov. 2025.

Issue

Vol. 6 No. 10 (2018): IJCSE October Edition

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.