Insights from Machine Learning Models: Sentiment Trends on X (Formerly Twitter)

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 12

Year of Publication : 2024

Authors : Poorva Agrawal, Charvi Kumar, Somesh Nagar, Saumya Deshmukh

10.14445/23488549/IJECE-V11I12P115

How to Cite?

Poorva Agrawal, Charvi Kumar, Somesh Nagar, Saumya Deshmukh, "Insights from Machine Learning Models: Sentiment Trends on X (Formerly Twitter)," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 12, pp. 154-163, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I12P115

Abstract:

X (formerly Twitter) has long been a platform that allows users to share their thoughts and beliefs and vent their more negative feelings on a plethora of subjects. In an age dominated by social media, where people online lay their emotions and opinions bare, the ability to utilize natural language processing methods to extract and assess sentiments from tweets has become crucial. Using machine learning models like Random Forest Classifier, Logistic Regression, and Naïve Bayes, which produced encouraging findings, the study technique includes data gathering, preprocessing, feature extraction, and sentiment categorization. After performing a thorough research of sentiment analysis of tweets, the paper delves into possible ramifications from a national security and surveillance perspective.

Keywords:

Social-media, Sentiments, Emotions, Machine learning, Analysis, Natural language processing, Random Forest, Naïve Bayes, Logistic Regression, National security, Surveillance, SDG 16.

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