• Omkar Korde
• Parth Sajjan
• Parth Desai
• Prajwal Dhumal
• H. R. Kulkarni, Sonika Kamthe

Social media platforms such as Twitter, Reddit, and financial news forums have become rich sources of real-time opinions and sentiments influencing stock price movements.This project focuses on analyzing social media data for predicting stock market trends using natural language processing (NLP) and machine learning techniques. Sentiment analysis models are applied to user-generated content to capture public emotions toward specific companies or market indices. The study integrates textual sentiment with historical market data for improved predictive accuracy. The proposed model leverages a hybrid approach combining sentiment scoring, feature extraction, and regression-based forecasting. Experimental results indicate that integrating social media sentiment significantly enhances short-term prediction accuracy compared to models based solely on historical prices. This project focuses on the analysis of social media data for stock market prediction using modern data mining, natural language processing (NLP), and machine learning (ML) techniques. Sentiment analysis is employed to quantify public emotions — such as optimism, fear, or uncertainty — expressed in textual posts. The extracted sentiment signals are then integrated with historical market data (stock prices, volumes, and trends) to develop a hybrid predictive framework. This framework combines deep learning models like Long Short-Term Memory (LSTM) networks and Hybrid ARIMA–LSTM architectures, capable of capturing both linear and nonlinear temporal dependencies.

Social Media, Stock Market Prediction, Sentiment Analysis, Machine Learning, Deep Learning, NLP.

"Analysis of Social Media for Stocks Market Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.263-268, January-2026, Available :http://www.jetir.org/papers/JETIRHG06028.pdf

"Analysis of Social Media for Stocks Market Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. pp263-268, January-2026, Available at : http://www.jetir.org/papers/JETIRHG06028.pdf