Enhancing Social Media Sentiment Analysis and Stress Detection Using Machine Learning

Enhancing Social Media Sentiment Analysis and  Stress Detection Using Machine Learning

B.Akshita1, M.Chetana Ashritha2, K.Sreelatha3
1      UG student, Dept. of Electronics and Computers Engineering, Sreenidhi Institute of Science and

Technologies, Telangana, India

2      UG student, Dept. of Electronics and Computers Engineering, Sreenidhi Institute of Science and

Technologies, Telangana, India

3     Asst. Professor, Dept. of Electronics and Computers Engineering, Sreenidhi Institute of Science and

Technologies, Telangana, India

20311a19d0@sreenidhi.edu.in, 20311a19e0@sreenidhi.edu.in, sreelathak@sreenidhi.edu.in

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Abstract - This project focuses on comprehensive framework that enhances social media sentiment analysis and stress detection using state-of-the-art machine learning techniques. It addresses the challenges of sentiment analysis by employing deep learning models like RNNs and transformers, achieving superior performance in sentiment classification across various social media platforms. Additionally, it proposes a novel hybrid approach for stress detection by combining lexical analysis, sentiment analysis, and physiological signals, ensuring robustness across different demographics and cultural contexts while emphasizing ethical considerations in handling sensitive user data. Keywords: Sentiment Analysis, Opinion Mining, Text Mining, Machine Learning, Deep Learning, Lexicon-based Methods, Social Media Analysis, E-commerce, Healthcare, Public Opinion Analysis, Challenges, Ethical Implications.