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@article{173570,
        author = {Faizal Ikheriya and ZIshankhan Pathan and Mohammed Rehan Shaikh and Mohammedzaid Bhatti and Dr. Khyati Zalawadia and Tejas Rana and Dhruvin Kotak},
        title = {Leveraging LSTM for Sentiment Analysis of Financial Market News},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {755-760},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173570},
        abstract = {Investor sentiment plays a pivotal role in influencing financial markets and can be effectively analyzed using news headlines. This research utilizes Machine Learning (ML) and Natural Language Processing (NLP) techniques to classify sentiment in financial news headlines, with the goal of improving market predictions and trading strategies. The dataset consists of daily financial news headlines from January 1, 2010, to August 27, 2010, labeled as either negative or positive. Sentiment classification is conducted using several models, including Logistic Regression, Naïve Bayes, and BERT. The findings reveal that sentiment analysis offers valuable insights into market behavior, highlighting the significant potential of AI in aiding financial decision-making. This study provides a comprehensive overview of the methods, challenges, and practical applications of sentiment analysis in the financial domain.},
        keywords = {},
        month = {March},
        }