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@article{176900,
        author = {Karthikeyan N and Jaswanth Kumar S and Dheepanraj S R and Babisha A},
        title = {Sentiment Driven Market Prediction: Evaluating News Influence On Stock Performance},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {6788-6794},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176900},
        abstract = {In recent years, sentiment analysis has emerged as a powerful tool for understanding public perception and its impact on stock market behavior. This study advances traditional sentiment-based stock prediction by integrating cutting-edge AI techniques, feature engineering, and predictive modeling. We employ web scraping to collect financial news and apply transformer-based sentiment analysis models to extract nuanced sentiment insights. Aggregated sentiment scores, combined with technical stock indicators, form a comprehensive feature set to analyze the correlation between market sentiment and stock prices over time. For predictive modeling, we utilize hybrid approaches, including sentiment-augmented time-series models, deep learning architectures, and ensemble methods. A Long Short-Term Memory (LSTM) network is trained to predict stock price movements based on historical stock prices and sentiment scores. Trading signals are generated based on the predicted prices and sentiment trends, providing actionable buy, sell, or hold recommendations. The model's performance is evaluated using metrics such as Mean Absolute Error (MAE) and directional accuracy, ensuring robust and reliable predictions. The findings are integrated into an interactive dashboard, enabling traders and investors to make informed decisions based on sentiment-driven market signals. This data-driven investment tool enhances decision-making, offering actionable insights to traders and investors in volatile stock environments.},
        keywords = {Sentiment Analysis, Stock Market Prediction, Financial News Scraping, LSTM, Transformer Models, Trading Signals.},
        month = {April},
        }