Using Machine Learning to Predict the Sentiment of Tweets Related to Artificial Intelligence in the Workplace

Tabish Khan , Bushra Rehman , Shivani Gupta

Artificial Intelligence, Sentiment Analysis, Machine Learning, Twitter Data, Workplace Automation, Public Perception

The rise of AI at work has led to many people sharing their thoughts on social media, mostly on Twitter. The research aims to study the use of machine learning to discover and forecast how people in professional environments feel about AI. There was a collection, preparation, and cleaning of a substantial dataset of AI-related tweets using natural language processing techniques. Among the tasks was cleaning the text, turning it into word tokens, and putting it in a format that machine learning can use. Support Vector Machine (SVM), Logistic Regression, and Random Forest were constructed and tested to check how accurately they could identify the sentiment polarity in a piece of content. That particular model performed well in distinguishing different sentiments from each other. It seems that people are divided in their opinion. although some users appreciate the possibilities brought by AI efficiency and new inventions, there are still concerns about job security and ethics. They can help inform business leaders and policy makers by making it clearer how people see the impact of automation and intelligent systems.

Published Paper   E-Certificate

"Using Machine Learning to Predict the Sentiment of Tweets Related to Artificial Intelligence in the Workplace ", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 5, page no.b784-b787, May-2025, Available :https://ijsdr.org/papers/IJSDR2505191.pdf

Volume 10 Issue 5, May-2025

Pages : b784-b787

Paper Reg. ID: IJSDR_302889

Published Paper Id: IJSDR2505191

Downloads: 000476

Research Area: Science and Technology

Country: South Delhi, Delhi, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2505191

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2505191

DOI: https://doi.org/10.56975/ijsdr.v10i5.302889

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition