Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{154755,
        author = {Nir Nandu and Taibaz Pathan and Rahul Shukla and Kanhaiya Singh and Payal Varangaonkar},
        title = {Stock Price Prediction Using Sentiment Analysis },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {12},
        pages = {229-233},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154755},
        abstract = {Machine learning is THE future. As our hardware systems inch ever so forward so does our capability in deploying ever increasingly more complex machine learning algorithms. With the failure of the efficient market hypothesis and more advent of more powerful processors stock market prediction has become a viable solution. By taking current news sentiments and combining them with technical analysis of price factors, this research aims to predict trends in future prices on an intraday basis. It is human nature to be more focused on the negative parts of a dialogue. So, the sentiment analyzer was made to be more sensitive towards negative news. The predicted values showed a certain trend in which they were averse to rising too quickly. We think this is because the model was made to be more prone to negative things. This coupled with the more negative focused sentiment analyzer made it such that the model took a rather conservative or even pessimistic approach in its prediction. It was found that considering the news polarity drastically reduces the MSE values to below 1. This just shows us how much the general public relies on the news headlines to navigate the stock market and that just technical or fundamental analysis isn’t enough for accurate predictions. },
        keywords = {},
        month = {},
        }