Market Trend Discovery Through Deep ‎Learning-Driven Twitter Sentiment for ‎Stock Forecasting

Authors

  • Mr. Lukesh Kadu Research Scholar, Computer Engineering Department, A. C. Patil College of Engineering, Navi Mumbai
  • Dr. M. M. Deshpande HOD, Computer Engineering Department, A. C. Patil College of Engineering, Kharghar, Navi Mumbai
  • Dr. V. N. Pawar Principal, A. C. Patil College of Engineering, Kharghar, Navi Mumbai

DOI:

https://doi.org/10.14419/c1qpgz81

Published

15-11-2025

Keywords:

Sentiment Analysis; Deep Learning; Stock Pre-Diction; Twitter; LSTM; BERT

Abstract

Short-range stock price forecasting presents considerable challenges due to the multifaceted and rapidly evolving nature of market signals. ‎Twitter, as a social media platform, delivers immediate public sentiment, serving as a valuable complement to traditional price-based metrics. This research introduces an operational framework merging Twitter sentiment classification with time-series forecasting that integrates ‎both sentiment and technical market features. The sentiment analysis component employs an ensemble approach combining LSTM, CNN, ‎and fine-tuned BERT architectures. These sentiment indicators feed into a neural network designed for time-series analysis to forecast directional price movements. Testing on datasets from January 2020 through December 2022 yielded 87.3% accuracy in sentiment classification ‎and 82.1% accuracy in directional prediction. These outcomes indicate that well-processed social media sentiment enhances short-term trading signals when combined with conventional technical indicators.

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How to Cite

Kadu , M. L. ., Deshpande , D. M. M. ., & Pawar , D. V. N. . (2025). Market Trend Discovery Through Deep ‎Learning-Driven Twitter Sentiment for ‎Stock Forecasting. International Journal of Basic and Applied Sciences, 14(7), 388-395. https://doi.org/10.14419/c1qpgz81

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