StockSense: A Real-Time Stock Market Sentiment Analysis

• Author(s): Vinay A; Gasi Jaswanth; Likhith N; Adarsh Shankar; Prof. Bhaskar M G
• Paper ID: 1719155
• Page: 2856-2863
• Published Date: 26-06-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 12 June-2026
• DOI: https://doi.org/10.64388/IREV9I12-1719155

Abstract

This paper presents StockSense, a full-stack web-based dashboard for real-time stock market analysis that integrates Natural Language Processing (NLP)-driven news sentiment scoring with walk-forward machine learning price forecasting. The system is implemented as a Flask backend serving a dynamic dark-themed frontend. For NLP, headlines are fetched live from Google News RSS and processed by VADER and TextBlob, producing per-headline sentiment scores that are aggregated into financial, news, and combined sentiment channels. Technical analysis — including RSI, MACD, Bollinger Bands, and moving averages — is computed on historical price data retrieved via the yfinance API. A Gradient Boosting Regressor trained on 18 engineered features using a strictly chronological 80/20 train-test split performs multi-step walk-forward price forecasting of up to 30 sessions, with no look-ahead leakage. Evaluated across multiple NSE/BSE-listed stocks (2-year window), the model consistently achieves R² above 0.99, sub-1% MAPE, and direction accuracy exceeding the 50% random baseline. The dashboard also provides stock fundamentals, a rule-based recommendation engine, and a context-aware chat assistant. StockSense demonstrates how NLP and ML can be combined in a practical, deployable educational tool for retail investors.

Keywords

Gradient Boosting, Machine Learning, Natural Language Processing, Sentiment Analysis, Stock Market Analysis, Technical Indicators, Textblob, VADER, Walk-Forward Forecasting.

Citations

IRE Journals:
Vinay A, Gasi Jaswanth, Likhith N, Adarsh Shankar, Prof. Bhaskar M G "StockSense: A Real-Time Stock Market Sentiment Analysis" Iconic Research And Engineering Journals Volume 9 Issue 12 2026 Page 2856-2863 https://doi.org/10.64388/IREV9I12-1719155

IEEE:
Vinay A, Gasi Jaswanth, Likhith N, Adarsh Shankar, Prof. Bhaskar M G "StockSense: A Real-Time Stock Market Sentiment Analysis" Iconic Research And Engineering Journals, vol. 9, no. 12, Jun. 2026, doi: https://doi.org/10.64388/IREV9I12-1719155

APA:
Vinay A, Gasi Jaswanth, Likhith N, Adarsh Shankar, Prof. Bhaskar M G (2026). StockSense: A Real-Time Stock Market Sentiment Analysis. Iconic Research And Engineering Journals, 9(12). doi: https://doi.org/10.64388/IREV9I12-1719155

MLA:
Vinay A, Gasi Jaswanth, Likhith N, Adarsh Shankar, Prof. Bhaskar M G "StockSense: A Real-Time Stock Market Sentiment Analysis" Iconic Research And Engineering Journals, vol. 9, no. 12, Jun. 2026. Crossref, https://doi.org/10.64388/IREV9I12-1719155

BibTeX

@article{1719155,
author = {Vinay A, Gasi Jaswanth, Likhith N, Adarsh Shankar, Prof. Bhaskar M G},
title = {StockSense: A Real-Time Stock Market Sentiment Analysis},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {12},
pages = {2856-2863},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719155.pdf},
abstract = {This paper presents StockSense, a full-stack web-based dashboard for real-time stock market analysis that integrates Natural Language Processing (NLP)-driven news sentiment scoring with walk-forward machine learning price forecasting. The system is implemented as a Flask backend serving a dynamic dark-themed frontend. For NLP, headlines are fetched live from Google News RSS and processed by VADER and TextBlob, producing per-headline sentiment scores that are aggregated into financial, news, and combined sentiment channels. Technical analysis — including RSI, MACD, Bollinger Bands, and moving averages — is computed on historical price data retrieved via the yfinance API. A Gradient Boosting Regressor trained on 18 engineered features using a strictly chronological 80/20 train-test split performs multi-step walk-forward price forecasting of up to 30 sessions, with no look-ahead leakage. Evaluated across multiple NSE/BSE-listed stocks (2-year window), the model consistently achieves R² above 0.99, sub-1% MAPE, and direction accuracy exceeding the 50% random baseline. The dashboard also provides stock fundamentals, a rule-based recommendation engine, and a context-aware chat assistant. StockSense demonstrates how NLP and ML can be combined in a practical, deployable educational tool for retail investors.},
keywords = {Gradient Boosting, Machine Learning, Natural Language Processing, Sentiment Analysis, Stock Market Analysis, Technical Indicators, Textblob, VADER, Walk-Forward Forecasting.},
month = {June}
}