The application is a cloud service that provides the functionality of performing sentiment analysis on stock market and financial data. The application can be hosted on Google App Engine and makes use of many of the GAE services like Search Service, MemCache, DataStore etc. Given the name of a company, data from various sources like Twitter, Facebook Graph, Google News, Google Finance etc is aggregated. For each source, different models have been pretrained using some prior data. Using different models provided us with a chance to utilize different Machine Learning methodologies based on the type of data from each source. The various techniques that we have built and tested on are :Naive Bayes, Multinomial and Bernoulli text representations, KNN.

It used to take days for financial news to spread via radio, newspapers, and word of mouth. Now, in the age of the internet, it takes seconds. Did you know news articles are automatically being generated from figures and earnings call streams? In this project, you will generate investing insight by applying sentiment analysis on financial news headlines from Finviz. Using this natural language processing technique, you will understand the emotion behind the headlines and predict whether the market feels good or bad about a stock. This project lets you apply the skills from Intermediate Python for Data Science, Manipulating DataFrames with pandas, and Natural Language Processing Fundamentals in Python. We recommend that you take those courses before starting this project. Familiarity with the Beautiful Soup package may also be helpful. The datasets used in this project are raw HTML files for the Facebook (FB) and Tesla (TSLA) stocks from FINVIZ.com, a popular website dedicated to stock information and news.

Stock.ai is a comprehensive platform for predicting stock market movements using machine learning models, real-time data analysis, and sentiment analysis of financial news. The system continuously learns from its predictions to improve accuracy over time.

Financial and economic news is continuously monitored by financial market participants. According to the efficient market hypothesis, all past information is reflected in stock prices and new information is instantaneously absorbed in determining future stock prices. Hence, prompt extraction of positive or negative sentiments from news is very important for investment decision-making by traders, portfolio managers and investors. Sentiment analysis models can provide an efficient method for extracting actionable signals from the news. However, financial sentiment analysis is challenging due to domain-specific language and unavailability of large labeled datasets. General sentiment analysis models are ineffective when applied to specific domains such as finance. To overcome these challenges, an evaluation platform which is used to assess the effectiveness and performance of various sentiment analysis approaches, based on combinations of text representation methods and machine-learning classifiers.

Sentiment Analysis of Financial News in Python for Stock Market Prediction

Project to cluster SP500 stocks based on their daily market value, sentiment analysis of stock market, and topic modeling of financial news to separate stock market related news for the last 1 year and 5 years.

Real-time stock market data pipeline utilizing FinBERT for sentiment analysis of financial news. Built with PostgreSQL (Neon), and GitHub Actions for automated Monthly ingestion.

This project predicts stock prices based on historical data, market trends, and news sentiment analysis using machine learning. The model uses LSTM (Long Short-Term Memory) for time series prediction and NLP (Natural Language Processing) for sentiment analysis of financial news.

This project predicts NVIDIA's stock prices using XGBoost and TFT by combining historical market data with sentiment analysis of financial news, featuring time-series optimization and rolling forecasts for multi-day predictions

Financial news headlines offer valuable NLP data for predicting stock performance through sentiment analysis. In Finance and Banking, language serves as a key tool for analysis, capturing emotional nuances that influence investor behavior and market dynamics. The project focuses on understanding how the subtleties of financial language impact diffe

It used to take days for financial news to spread via radio, newspapers, and word of mouth. Now, in the age of the internet, it takes seconds. Did you know news articles are automatically being generated from figures and earnings call streams? In this project, you will generate investing insight by applying sentiment analysis on financial news headlines from Finviz. Using this natural language processing technique, you will understand the emotion behind the headlines and predict whether the market feels good or bad about a stock. The datasets used in this project are raw HTML files for the Facebook (FB) and Tesla (TSLA) stocks from FINVIZ.com, a popular website dedicated to stock information and news.

It used to take days for financial news to spread via radio, newspapers, and word of mouth. Now, in the age of the internet, it takes seconds. Did you know news articles are automatically being generated from figures and earnings call streams? In this project, I generated investing insight by applying sentiment analysis on financial news headlines from Finviz. Using this natural language processing technique, I have also understood the emotion behind the headlines and predict whether the market feels good or bad about a stock. The datasets used in this project are raw HTML files for the Facebook (FB) and Tesla (TSLA) stocks from FINVIZ.com, a popular website dedicated to stock information and news.

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.

Sentiment analysis of financial news for stock market prediction

AI-powered sentiment analysis of financial news and tweets for stock market insights.

Stock market prediction using Python with LSTM models for price forecasting and sentiment analysis of financial news

Financial News Stock Analysis Repository: Your go-to resource for analyzing financial news and stock market trends. Includes tools for sentiment analysis, predictive modeling, and deep insights into the world of finance and investments

This project examines target-level financial sentiment analysis of news headlines for stock companies like Amazon, Netflix, Nvidia, and Alphabet. It compares traditional sentiment analysis methods with advanced large language models, using a curated Bloomberg Terminal dataset to understand how financial news sentiment affects market perceptions.

A project that combines LSTM-based stock price prediction with sentiment analysis of market news. It provides insights into future stock trends and actionable recommendations for better financial decision-making.

automated sentiment analysis pipelineor stock market-related text data. Financial news and social media chatter often correlate with market trends. By classifying these texts into **Positive (1)** or **Negative (0)** sentiments, investors can gauge the general mood of the market for specific assets.

TRADESCOPEPRO is a powerful web-based stock market dashboard designed for Indian markets. It offers real-time stock price updates, advanced interactive charting, forex/global instruments, live market heatmaps, and AI-based sentiment analysis of financial news — delivering a professional trading-terminal experience using pure frontend technologies.

This repository contains a comprehensive solution for predicting stock prices using a combination of data science techniques, machine learning models, and financial analysis. The goal of this project is to forecast future closing stock prices based on historical market data, technical indicators, and news sentiment analysis.

Developed a scalable real-time stock recommendation system using Python, Spark, Hadoop, and Docker on Ubuntu. Processed millions of streaming data points for accurate stock predictions. Integrated a risk assessment module with sentiment analysis of 500+ financial news articles, enhancing prediction accuracy and providing actionable market insights.

This is an interactive **Streamlit** web application designed to provide key financial metrics for the IT sector. The dashboard aggregates stock performance, exchange rates, and IT & Tech news with sentiment analysis to give a holistic view of market conditions.

It used to take days for financial news to spread via radio, newspapers, and word of mouth. Now, in the age of the internet, it takes seconds. Did you know news articles are automatically being generated from figures and earnings call streams? In this project, you will generate investing insight by applying sentiment analysis on financial news headlines from Finviz. Using this natural language processing technique, you will understand the emotion behind the headlines and predict whether the market feels good or bad about a stock. The datasets used in this project are raw HTML files for the Facebook (FB) and Tesla (TSLA) stocks from FINVIZ.com, a popular website dedicated to stock information and news.