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

LSTM-RNN Tutorial with LSTM and RNN Tutorial with Demo with Demo Projects such as Stock/Bitcoin Time Series Prediction, Sentiment Analysis, Music Generation using Keras-Tensorflow

Stock price prediction using Bidirectional LSTM and sentiment analysis

Stock price prediction with LSTM adding sentiment analysis of some associated online news.

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 trajectory analysis and prediction using machine learning techniques like sentiment analysis and long short-term memory (LSTM) neural nets. Frontend implemented as a Discord bot.

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

This study is about creating a sensitivity classifier model using messages from customers. We have a binary classification problem that categorizes stock sensitivity data as positive or negative. 1 indicates positive sentiment and 0 indicates negative sentiment. The main resource I used in the study is the Python & Machine Learning for Financial Analysis course on Udemy. The main steps are as follows: Importing required libraries(pandas,numpy,seaborn,matplotlib,nltk,gensim,tensorflow) Explanatory Data Analysis Data cleaning (removing punctuations and stopwords from text) Visualization of cleaned dataset and plotting wordcloud Prepare the data by tokenizing and padding Building a custom-based deep neural network for sentiment analysis (embedding layer, LSTM network) Making prediction and assessing the model performance (confusion matrix)

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.

A novel stock market prediction model using GANs enhanced with sentiment analysis. Combines historical stock data and social media sentiment through VAE feature extraction, LSTM generator, and CNN discriminator for improved accuracy

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

The following code uses sentiment analysis on the Times News Headlines along with stock data of the AAPL ticker to make predictions. Sentiment Analysis is done using NLTK and the model is based on LSTMs.

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.

This is a stock prediction project. Borsa Istanbul data has been retrieved from Yahoo Finance. NLP steps have been performed, and sentiment analysis has been conducted using BERT. RSI and MACD technical indicators have been calculated. The generaal uptrend also used. LSTM MODEL

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.

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.

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.

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.

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.

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.