Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall

Stock Market Trend Prediction using sentiment analysis Leveraging machine learning and sentiment analysis, we accurately forecast stock market trends. Our project combines advanced algorithms like BERT and Naïve Bayes with sentiment analysis from Twitter and other sources. By analyzing sentiment and historical price data, we provide insights

Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets **(API keys included in code)**. The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are given for three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices for the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall

Stock price prediction visualization app powered by LSTMs for forecasting and FinBERT for sentiment analysis

A machine learning-driven platform that integrates sentiment analysis and advanced predictive modeling to forecast stock market trends. It leverages TensorFlow, financial news analysis, and technical indicators within a Django web interface to offer insightful stock price predictions.

Developed an end-to-end stock price prediction model by integrating LLM-based sentiment analysis of financial news with time series forecasting, leveraging Python, TensorFlow, and Hugging Face Transformers; achieved enhanced prediction accuracy by incorporating sentiment data.

This project was completed with the intention of helping Tesla stock investors better understand how to make decisions where the stock market is very volatile by training different models through historical and social media data analytics. Behavioral economics shows that public emotions can profoundly affect individual behavior and decision making. In order for investors to utilize it, business analysts must understand the behaviors and attitudes of the public within the finance context. Nowadays, social media perfectly tracked by data reflects the public emotions and sentiment about stock movement. Also, tremendous stock marketing news can be used to capture a trend of stock movement. The fundamental trading and decision making for main techniques rely on expert training and prediction. This article concentrated on tweets and stock news, and I applied sentiment analysis and machine learning models, especially, XGBoost to tweets and news extracted from Elon Musk tweets, Nasdaq and New York Times News about Tesla. Only by understanding the values and priorities of the public sentiment of Tesla stock will investors be able to make significant decisions. In addition, I conducted two models- ARIMA and RNN(LSTM) in forecasting the Tesla stock price. I compare their results with the prediction performances of the classical ARIMA and RNN.

Apple Stock Price Forecasting using Sentiment Analysis

Machine learning model to predict stock price fluctuations. Uses traditional forecasting methods and social media sentiment analysis.

Advanced stock forecasting system using LSTM neural networks with real-time sentiment analysis. Predicts price movements and volatility by combining technical indicators, news sentiment from Finnhub API, and multivariate analysis. Features dual LSTM models, intelligent alerts, and comprehensive risk assessment for informed trading decisions.

Merging machine learning models (Regression) with Sentiment Analysis in order to forecast stock price and support buy-sell operations.

Forecasting the stock prices using generative adversarial networks and sentiment analysis of social networks [MDES vol.10 2022]

📈 Suite of a stock price forecaster and a sentiment analysis program built to provide in-depth visualizations, high accuracy, and low resource usage.

StockSense: Your ultimate AI Trading app, Elevate your trading game with precise stock price forecasts and real-time sentiment analysis of news headlines. Enjoy faster data retrieval by seamless database integration, maximizing productivity and keeping you ahead of market trends. 📈🤖

Stock Price Prediction Using TFMS Analysis is a comprehensive project aimed at forecasting stock prices by integrating multiple analytical approaches. This includes technical analysis, fundamental analysis, machine learning techniques, and sentiment analysis.

Developing a hybrid time-series forecasting model which incorporates the influence of external phenomenon through sentiment analysis of financial news headlines to forecast closing stock prices of bombay stock exchange.

Developed a time series model to predict next-day stock prices by integrating historical stock price data with real-time news sentiment analysis, enhancing forecast accuracy through multimodal data fusion.

FinSight leverages LSTM networks and FinBERT sentiment analysis to predict stock prices with enhanced accuracy, integrating financial news sentiment for more informed and precise market forecasts.

Scraped and analyzed 10,000+ tweets for sentiment using FinBERT. Built predictive models with XGBoost (R² = 0.864) to forecast stock price movement. Achieved a 15% improvement over baseline with engineered financial and sentiment features.

Explore NVIDIA's stock dynamics with this project, using a mix of traditional and deep learning models to forecast stock prices and analyze the influence of market sentiment. Integrates ARIMA, LSTM, and more to provide deep insights.

This project is a hybrid stock forecasting system that combines deep learning, technical indicators, and news sentiment to predict prices of major automotive stocks, offering a smart, data-driven tool for investors.

This project focuses on predicting Google’s stock price using a combination of fundamental and technical analysis. By examining financial metrics, earnings reports, and market trends, alongside sentiment analysis from news and social media, we aim to identify patterns and forecast future price movements

The program forecasts stock prices by combining sentiment analysis with an LSTM neural network. Sentiment scores derived from BERT and VADER, analyzing financial news and social media, are integrated with historical data of Apple Inc., etc. This enriched dataset feeds into a Keras-built LSTM model.

A full-stack AI-powered financial analytics pipeline that ingests, processes, and analyzes real-time stock market data using Kafka, PySpark, DuckDB, and machine learning models. The system provides real-time stock price forecasting and sentiment analysis of financial news, accessible via an API and visualized through a Streamlit dashboard.

I propose a project to analyze how Twitter can help to forecast the movement of the stock price from a particular public company. Rather than using news, seeing the reaction from people on Twitter will give more effective "early warning" to companies for anticipating any negative issue that might harm its businesses. It will also help the companies to identify which kind of issue that might give huge or low impact to them. Sentiment analysis could be added as well for future development. The dataset that will be used is the historical stock price from Yahoo Finance for "Tesla" and scraping from Twitter.

It is no wonder that how our favorite coffee shop Starbucks employs data analytics and business intelligence techniques to deliver excellent customer service. This is the largest and famous coffee chain which has become one of the places which uses data analytics and enterprise applications in intersection. This report illustrates how behind a freshly prepared cup of coffee there is an insightful corporate tactic and how factors like weather conditions and twitter sentiments affect the location and stocks of Starbuck stores. Predicting stock prices based on twitter sentiments data would produce strong buy or not is still a debatable topic over the years and making it more difficult to forecast accurately. Data analytics also plays a key role in determining the best location for new stores. In this study, for data extraction APIs were used to extract Twitter Sentiment and weather condition and Starbucks Location dataset was taken from Kaggle in csv format. After Data Transformations and Data Loading, a data warehouse was created for further analysis. Using our analysis, a significant dependency of all these datasets is identified using python libraries. For data storage MongoDB and SQL were used.It is no wonder that how our favorite coffee shop Starbucks employs data analytics and business intelligence techniques to deliver excellent customer service. This is the largest and famous coffee chain which has become one of the places which uses data analytics and enterprise applications in intersection. This report illustrates how behind a freshly prepared cup of coffee there is an insightful corporate tactic and how factors like weather conditions and twitter sentiments affect the location and stocks of Starbuck stores. Predicting stock prices based on twitter sentiments data would produce strong buy or not is still a debatable topic over the years and making it more difficult to forecast accurately. Data analytics also plays a key role in determining the best location for new stores. In this study, for data extraction APIs were used to extract Twitter Sentiment and weather condition and Starbucks Location dataset was taken from Kaggle in csv format. After Data Transformations and Data Loading, a data warehouse was created for further analysis. Using our analysis, a significant dependency of all these datasets is identified using python libraries. For data storage MongoDB and SQL were used.

Stock Price Forecast with LSTM using Sentiment Analysis

Microsoft Stock Price Prediction with Time Series Forecasting and sentiment analysis

A project combining GANs, Sentiment Analysis, and Temporal Forecasting for stock price prediction

Stock market price prediction project. It can forecast the price of stock using sentiment analysis, ARIMA model and LSTM model