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Sentiment-based Market Trend Analysis Using Social Media Data

 

 

Jaywardhan yadav…(Department of Data Science Dr. D. Y. Patil Arts, Commerce and Science College Pimpri)

Tushar Choudhari … (Department of Data Science Dr. D. Y. Patil Arts, Commerce and Science College Pimpri)

 

 

Abstract
This study investigates the use of social media sentiment to enhance stock market movement prediction. Platforms like Twitter (X), Reddit, and StockTwits contain emotional cues

reflecting public opinion on financial events. By collecting, cleaning, and aligning posts with stock price data, the research examines whether sentiment improves short-term

forecasting. Two models—VADER (rulebased) and FinBERT (deep learning)—were used for sentiment scoring. Sentiment indices were then combined with technical indicators and

analyzed using ARIMA, XGBoost, and LSTM models. Results show that sentimentenhanced models yield higher predictive accuracy than price-only approaches. The study highlights how integrating public mood signals with market data supports better decision-making in finance.

Key Words:
Stock prediction, sentiment analysis, FinBERT, VADER, XGBoost, LSTM, financial forecasting.

 

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