• Rudra Pratap Singh
• Siddharth

The prediction of stock market trends and prices has been a significant topic of interest for analysts and researchers over an extended period. The volatile nature of stock prices, influenced by economic factors, changes in leadership, investor sentiment, and other variables, presents a challenge in accurately forecasting them. Relying solely on data or textual information has proven to be inadequate for predicting stock prices. This study aims to explore the impact of news articles on stock market trends by investigating their relationship. To accomplish this, models such as LSTM are employed to determine whether a given news article carries a positive or negative sentiment. The findings reveal that both the LSTM and RF models perform exceptionally well across all tests, while Naïve Bayes produces satisfactory results but is less effective compared to the other two models. Various experiments exploring different aspects of the proposed model consistently yield encouraging results

"SentimentX: Enhancing Stock Prediction through news Sentiment Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.g146-g157, July-2024, Available :http://www.jetir.org/papers/JETIR2407618.pdf

"SentimentX: Enhancing Stock Prediction through news Sentiment Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppg146-g157, July-2024, Available at : http://www.jetir.org/papers/JETIR2407618.pdf