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

The coding has been done on Python 3.65 using Jupyter Notebook. This program fetches LIVE data from TWITTER using Tweepy. Then we clean our data or tweets ( like removing special characters ). After that we perform sentiment analysis on the twitter data and plot it for better visualization. The we fetch the STOCK PRICE from yahoo.finance and add it to the data-set to perform prediction. We apply many machine learning algorithms like (random forest, MLPClassifier, logistic regression) and train our data-set. Then we perform prediction on untrained data and plot it with the real data and see the accuracy.

Stock price trend prediction with news sentiment analysis using deep learning

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 with LSTM adding sentiment analysis of some associated online news.

Compilation of technical analysis tools (EMA, Bollinger bands), fundamental analysis, machine learning models (LSTM, Random forest, ARIMA, GARCH, Markov Regime Switching), traditional stock prediction tools (Monte Carlo), sentiment analysis (NLP) as well as portfolio optimization, with purpose to provide a better understanding regarding possible future price movements of the analyzed stock and propose an optimal asset allocation.

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.

flask and react app for interacting with chatGPT from public domain and ability to upload private data and ask questions about it. also a LSTM model for stock prediction based on historical stock price, plus sentiment analysis with openAI

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 project combines machine learning and natural language processing to predict stock prices. By integrating historical market data with sentiment analysis of news headlines, the model aims to provide accurate and insightful predictions.

Enhanced real-time stock price prediction by 12% using sentiment analysis on 5M+ daily tweets via Twitter API, polygon.io, and yfinance; built a scalable pipeline with Google Cloud Pub/Sub, Apache Beam, Dataflow, and visualized insights with Google BigQuery and Looker Studio.

The purpose of this project is to investigate the effect of several variables on the prediction of stock prices with emphasis on the analysis of the correlation between stock price and news media. Moreover, prediction of future stock movement using the Convolutional Neural Network model allows the User to choose a company from the S&P 500 and predict the price movement of a company's stock on the next trading move based on current sentiment (Vader) from GoogleNews articles related to such company.

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') ```

i used the idea of sentiment analysis of Stock Tweets through machine learning models.Then implemented the idea by collecting sentiment data and stock price market data and built an SVM model for We used SVM model to suggest whether a person should buy or sell a share.

A Python-based stock market prediction application that combines historical price trends with real-time news sentiment analysis to generate more informed stock movement predictions.

A comprehensive system that combines web crawling, sentiment analysis, and deep learning to predict short-term stock price movements. The system integrates news sentiment with technical indicators to provide more accurate stock price predictions.

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.

LLM Financial advisor fine tuned Bert model ,its main job to read and analyze data from earning call transcripts, i got earning call transcripts pkl data , summarized it . Cleaned, analyzed, summarized and made sentiment analysis and labeled it with sentiment type and stock price prediction.

Abstract In this project we propose a learning-based stock market information system for stock prices prediction and customer satisfaction estimation to help stock market clients decide their investment. For future prices prediction, we use time series models that are trained using historical stock data of different companies. Particularly, ARIMA model is used for monthly prediction and FBprophet model is used for daily prediction. For customer satisfaction estimation, we analyze and process customer reviewing tweets using sentiment analysis and natural language processing to predict a score indicating the percentage of satisfaction about certain company. Also, in this project, we propose an enhanced approach that combines customer reviews with historical data to improve stock prices prediction using convolutional neural networks or polynomial regression. Model results are analyzed, and comparisons are made between different approaches to determine best model to be adopted for each problem. Finally, we develop a web application with suitable user interface and expressive analyzing graphs for convenient use of our system. As a proof of concept, all of our work is practically applied on stocks and reviewing tweets of different ten global companies.

• create a website that displays numerical data, predictions about the numerical data and the results of sentiment analysis. • The numerical data will be obtained from web services. It cannot be obtained from web scraping. For example, it could be product price data from web services, stock prices, exchange rate prices, weather, football results, etc. • The text data for sentiment analysis will be obtained from web services, such as the Twitter API or Facebook Graph. • Machine learning will be used to make predictions about future values of the data. • Also display synthetic data that we will provide to check data visualization and machine learning. • All third party data will be stored in the cloud. • The front end of the website only has to display visualizations of the data, predictions about the data and the results of the sentiment analysis. No other functionality is required. • The code that downloads data from web services and uploads it to the cloud must be written in TypeScript. • Website will be hosted on the cloud using serverless technology. Lambda functions on the server can be written in any programming language (JavaScript is recommended). • The front end of website can use ordinary JavaScript or a JavaScript framework. • WebSockets will push new data items to subscribed clients. • The coursework and teaching materials will be based on Amazon Web Services (AWS). use a different cloud provider. However, we will only be able to provide very limited support with projects that are based on a different cloud provider.

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This repository hosts a comprehensive financial data analysis and stock prediction project that integrates machine learning techniques with web scraping and sentiment analysis. The project focuses on leveraging historical financial data and market sentiment to predict stock prices, assess investment risks, and analyze market trends effectively.

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Achieved 98% accuracy by integrating historical stock prices with real-time Twitter sentiment using GANs. Built a Flask-based web app for real-time predictions & visual insights. Tech Stack: Python, TensorFlow, Flask, NLP, Web Scraping. Impact: Enhanced trading decisions with AI-driven insights.

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This project examined the correlation between the twitter sentiments of selected stocks and their prices movements