For Chinese comments, the Finbert model was used to conduct polarity analysis and predict stock price rise

An AI framework combining FinBERT sentiment embeddings and LSTM-based stock trend modeling for real-time stock movement prediction using financial news and historical stock data.

An LSTM model to predict stock price trends using FinBERT sentiment analysis on financial news.

A Python-based pipeline for collecting Indian stock market news, mapping articles to NSE tickers, and performing FinBERT-based sentiment analysis with stock-wise trend visualization.

Collected financial news via NYT API and NASDAQ stock data via yfinance, extracted sentiment scores using FinBERT, merged them with stock data, and trained an LSTM model for stock trend prediction.

This project proposes a stock price prediction model that integrates financial news data with historical price signals, reframing prediction as a sentiment-based classification task. It utilizes an Average True Range (ATR)-based approach for labeling market sentiment and FinBERT, to enhance alignment with market trends.

This project integrates financial news sentiment analysis with stock trend prediction using an LSTM (Long Short-Term Memory) neural network. It utilizes FinBERT, a financial sentiment analysis model, to extract sentiment scores from financial news and combines them with stock price trends to forecast future stock movements.

This project is a **real-time financial news sentiment analysis system** based on the **FinBERT** pre-trained model. It automatically analyzes the impact of financial news on stock prices and predicts stock price movement trends.

Stock trend prediction (Uptrend/Downtrend/Flat) for Bursa Malaysia using financial news sentiment. A comparative study between LSTM with GloVe embeddings and a fine-tuned FinBERT model.

No description available

No description available

Built an NLP system using FinBERT to analyse financial news and generate BUY/SELL/HOLD signals. Developed the full pipeline from automated news collection to sentiment classification, demonstrating strong skills in NLP and financial prediction.

AI-powered stock trend forecaster using LSTM and FinBERT sentiment analysis.

Stock price trend prediction using LSTM and FinBERT sentiment analysis built with Streamlit.

This project explores the effectiveness of combining **historical stock price data** with **news sentiment analysis** to predict short-term stock market trends. The hybrid model leverages: LSTM, FinBERT

An AI-powered system that combines FinBERT-based sentiment analysis and LSTM time-series forecasting to support smarter stock investment decisions using financial news, social media trends, and historical stock data.

Predict gold stock price trends while maintaining data privacy. Combine Federated Learning, Bi-LSTM, and FinBERT sentiment analysis. Provide an accurate and privacy-aware forecasting system.

FinTech Stock Trend Predictor uses LSTM to forecast stock prices and FinBERT to analyze financial news sentiment. Users can view historical and predicted prices along with sentiment insights in an interactive Streamlit dashboard.

A real-time financial news sentiment dashboard that fetches headlines, analyzes sentiment with FinBERT, and visualizes trends alongside stock prices. Built with Next.js, Tailwind, and Vercel.

Sentiment analysis on financial news using NLP and TensorFlow. Includes preprocessing, FinBERT-based classification, and correlation with stock price trends. Built for real-world financial insights and deployment readiness.

A FastAPI-based web application that uses FinBERT to analyze the sentiment of financial news headlines and visualize correlated stock performance trends using live market data from TwelveData API.

CFX – Explainable Hybrid Stock Prediction System Combines candlestick pattern recognition, FinBERT sentiment analysis, and XGBoost models to predict NIFTY50 market trends with explainable AI insights — all in a beautiful Streamlit interface.

This is an in-depth analysis of stock market news using an LLM called finBERT. The aim is identify trends in sentiments, compare this with stock market trends and determine its predictive abilities. Further studies can be done to improve training the model and increasing it predictive accuracies

A Streamlit application that fetches news headlines for given stock tickers, performs sentiment analysis using FinBERT, and displays results in an interactive dashboard. Supports single‑ticker analysis and industry‑wide trend checks.

Developed an AI-powered financial analytics platform for automated stock sentiment analysis using FinBERT and BART. It collects real-time news, performs sentiment classification, summarization, keyword/NER extraction, and correlates trends with live stock data. Built an interactive dashboard with PDF reports.

An NLP-driven tool for financial news sentiment analysis. Features automated data collection for multiple stock tickers, sentiment scoring with FinBERT, persistent storage, and an interactive dashboard for visualizing sentiment trends by ticker and sector, with capabilities for stock price correlation analysis.

This is a Streamlit web app that performs sentiment analysis on recent stock-related news headlines. It uses the FinBERT model for financial sentiment scoring and visualizes price trends and news impact using Plotly charts.

FinX AI is an AI-powered financial analytics platform for stock, IPO, and portfolio analysis. It predicts stock trends using LSTM/GRU, estimates IPO gains with GMP and sentiment models, analyzes news via FinBERT, and optimizes portfolios using MPT and RL, all through an interactive dashboard.

This repository holds the datasets, scripts, and other content associated with the usage of NLP sentiment analysis model FinBert to make predictions about the sentiment of news headlines and to study how public opinion affects trends in the stock market

Hybrid deep learning model combining FinBERT for financial news sentiment and LSTM for stock price forecasting. Trained on Tesla, Apple, Microsoft, and NVIDIA data (2020–2024), achieving 82.47% accuracy and ROC-AUC ≈ 0.90, showcasing sentiment-driven market trend prediction.