Copyright © 2026 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{206364,
        author = {Duggi Shashikala and M.Maruthi rao and B. Mahesh Kumar and B.  Chandhu Lalitha Kumari},
        title = {A DEEP LEARNING AND INVESTOR SENTIMENT-BASED STOCK PRICE PREDICTION MODEL},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {1346-1350},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206364},
        abstract = {Accurate prediction of stock prices can reduce investment risks and increase returns. This project combines the multi-source data affecting stock prices and applies sentiment analysis, swarm intelligence algorithm, and deep learning to build the MS-SSA-LSTM model. Firstly, we crawl the East Money forum posts information to establish the unique sentiment dictionary and calculate the sentiment index. Then, the Sparrow Search Algorithm (SSA) optimizes the Long and Short-Term Memory network (LSTM) hyper parameters. Finally, the sentiment index and fundamental trading data are integrated, and LSTM is used to forecast stock prices in the future. Experiments demonstrate that the MS-SSA-LSTM model outperforms the others and has high universal applicability.},
        keywords = {MS SSA, Deep learning, LSTM, Share market, Prediction.},
        month = {July},
        }