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 price prediction using Bidirectional LSTM and sentiment analysis

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.

Stock_Market_Prediction using textual analysis : Applying the NLP processing and the Sentiment analysis to the textual dataset(news headlines) * Numericel analysis : Applying the numerical analysis to the historical stock prices dataset * merging the two datasets : Create a hybrid model for stock price/performance prediction using deep learning LSTM for time series forcasting with the avoidance of overfitting

In this project, we combine historical stock price data with sentiment analysis of financial news using FinBERT to predict stock prices for Apple Inc. (AAPL) using a Long Short-Term Memory (LSTM) neural network.

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.

Stock price prediction web application using LSTM and Sentiment Analysis of Tweets

An AI-powered stock price prediction tool built using Streamlit, LSTM neural networks, and sentiment analysis from news, Twitter, and Google Trends data.

This is a project based on stock market price prediction and news sentiment analysis using LSTM. The Sentiment analysis also contains NLTK Vader Sentiment Scores and BERT Sentiment Scores. The Backend and Web-application is designed using Flask framework and Java-Script, HTML & CSS

A stock price prediction model using Long Short-Term Memory (LSTM) neural networks combined with sentiment analysis of financial news articles. Developed using Python, Used TensorFlow, NLTK, and various data science libraries.

More than 90% of traders lose money on stock market because they fail to sync emotions with strategy to trade .Our approach of Stock Price prediction is one the way to solve the problem DJIA index prediction with LSTM-ARIMA hybrid model and News Sentiment Analysis . Achieved accuracy rate of 98.5 % on 75-25 Train Test Split. Combined News + Stock price data is large file size of around 227 MB. If can't download here's link to kaggle :https://www.kaggle.com/aaron7sun/stocknews Access the weights of LSTM ,ARIMA models individually .\ To use ARIMA model - Use command ``` loaded = ARIMAResults.load('arima_model.pkl') ``` \ To use LSTM model - Use command ``` model = tf.keras.models.load_model('lstm_model.h5') ```

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In this project to predict stock prices , we will use streamlit for web app , deep learning methods like Neural Networks RNN LSTM and use news and twitter data for Sentiment analysis.

Predicts the Close price of the NSE using LSTM with sentimental analysis

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

Apple stock price prediction by combining LSTM on stock price data and sentiment analysis on financial news of apple using FinBERT.

Hybrid deep learning stock prediction dashboard using LSTM for price forecasting and NLP-based sentiment analysis with OCR integration.

Explore stock price prediction using time series analysis and sentiment analysis. Leverage Hugging Face's Roberta model for sentiment analysis on financial news to gauge market sentiment. LSTM-based predictive model captures stock market patterns, while visualizations highlight the sentiment-stock price prediction for S&P 500.

A comprehensive stock prediction API that uses LSTM neural networks with sentiment analysis to predict stock prices. The API provides real-time predictions, sentiment analysis, and model performance metrics for various stock symbols.

AI-powered Indian stock market analysis and prediction tool using LSTM for price forecasting and FinBERT for news sentiment, featuring real-time data, fundamental analysis, and interactive dashboards.

A comprehensive web application for stock price prediction and sentiment analysis using Streamlit, LSTM models, and the Ollama model. This tool allows users to forecast stock prices, analyze market trends, compare stocks, and evaluate sentiment from news headlines

AI-powered stock analysis and prediction system using LSTM deep learning and sentiment analysis. Built with Python, Flask, TensorFlow/Keras, Pandas, and yFinance API to provide real-time stock data, price forecasting, and investment recommendations.

A web application for stock price prediction using Prophet (Bayesian model) and LSTM (Deep Learning). Built with Streamlit, Plotly, and OpenAI for sentiment analysis, it visualizes historical trends, predicts future stock movements, and analyzes financial news sentiment.

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 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.

A Python trading bot that combines LSTM neural networks for stock price prediction with real-time sentiment analysis from financial news. Uses Alpaca API for paper trading and Yahoo Finance for market data.

Developed an LSTM based prediction model in Python by leveraging historical time series data of stocks. Performed sentimental analysis on The Guardian daily news using DistilBERT and utilized sentiment value as an additional factor in predicting stock market prices. Predicted stock prices of Fortune 500 companies with 91 % accuracy.

BayesCast is a hybrid AI system for stock price prediction, centered on LSTM-based time-series modeling. It enhances deep learning forecasts with sentiment analysis and LLM-based market reasoning. Final predictions are fused using Bayesian Model Averaging to quantify uncertainty and generate confidence-weighted buy/hold/sell decisions.

Stock market is a place where shares of public listed companies are traded. Stock exchange facilitates stock brokers to trade company stocks and other securities. India's premier stock exchanges are the Bombay Stock Exchange and National stock exchange. Stock price prediction is one of the most widely studies and challenging problems, attracting researchers from many fields including economics, history, finance, mathematics and computer science. The model uses a collective of Three models: ARIMA, LSTM, Linear Regression. By calculating the root mean square error (RMSE) of each model for the enquired entity and select the model with the smallest RMSE. Then the selected model gets the predicted values for the next 7 days. calculate the mean of the selected days and if the mean is greater than today’s closing price then the stocks are on the rise and it will signal to buy. The second part of the project is the Sentiment analysis which will be done by using a Twitter API known as Tweepy. Using NLP to get the polarity of the tweets and calculate the number of positive or negative tweets. Depending on the result, the program will decide whether The Overall polarity is positive or negative.