Balancing Social Influence: Integrating Positive and Negative Opinions in  Online Networks

B. Sridhar, K. Chandu, L. Bhanu Prakash, M. Santhosh Reddy, M. Harsha Vardhan Reddy, M. Raju

Balancing Social Influence: Integrating Positive and Negative Opinions in Online Networks

Authors

B. Sridhar, K. Chandu, L. Bhanu Prakash, M. Santhosh Reddy, M. Harsha Vardhan Reddy, M. Raju

Keywords:

Fake News Detection, Tweet Classification, Natural Language Processing, Sentiment Analysis, Polarity Score, Tkinter GUI, Social Media Analytics

Abstract

This research presents a Tkinter-based application for classifying tweets as fake or authentic usingnatural language processing (NLP) and polarity score–based supervised learning. The system allowsusers to upload tweet datasets in CSV format and performs text preprocessing by removing punctuation, stopwords, and non-alphabetic characters

References

Malviya, S.; Tiwari, A.K.; Srivastava, R.; Tiwari, V. Machine learning techniques for sentiment analysis: A review. SAMRIDDHI A J. Phys. Sci. Eng. Technol. 2020, 12, 72–78.

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Published

2025-05-25

How to Cite

B. Sridhar, K. Chandu, L. Bhanu Prakash, M. Santhosh Reddy, M. Harsha Vardhan Reddy, M. Raju. (2025). Balancing Social Influence: Integrating Positive and Negative Opinions in Online Networks . Journal of Computational Analysis and Applications (JoCAAA), 34(5), 398–405. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4619