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

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 price prediction using long term trends, technical indicators and sentiment analysis

Stock market is an ideal way to invest hard earned money as it has the potential to provide great returns. But, even with the current technology at hand, it is a risky deed due to the inability to understand sudden market changes and interpret data appropriately. To ease the process of investment and to provide better awareness, we propose ‘Prediction of stock market deviation using ARIMA algorithm’: a real-time risk prediction software that considers market interests. It is based on a parametric time series analysis technique- ARIMA (Auto Regressive Integrated Moving Average) algorithm to interpret historic data. It also makes use of Sentiment analysis to convert market trends to valuable information. Since stock market is highly influenced by information release and public acceptance, the addition of Sentiment Analysis to ARIMA boosts system performance and provides a more accurate representation of market volatility. The software provides pictorial and graphical representations and can also be used to compare the growth of two companies for the required time period. The objective is to provide short term and long term prediction capabilities to prepare for future potential investments.

An integration of web scraping, XGBoost, and Apache Spark for AI-driven stock prediction and sentiment analysis. It extracts stock data from NSE, predicts prices using XGBoost, and analyzes news sentiment via Spark and NLTK. The model provides informed buy/sell decisions by combining historical trends and real-time market sentiment.

This Stock Market Analysis Dashboard provides a comprehensive view of stock market data, including real-time information, historical price trends, relevant news, and sentiment analysis. Using Yahoo Finance (via yfinance), NewsAPI, and XGBoost for stock price prediction

Stock market trend prediction and analysis using deep learning models and Visual representation along with sentiment analysis on stock market news . And Trading agents whcih plan investment strategy based on input of the best deep learning model.

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

Web-based stock market prediction application using machine learning models (Linear Regression, Random Forest, XGBoost) and sentiment analysis, built with Streamlit. It integrates real-time financial data, visualizes market trends, and provides predictions for S&P 500 companies.

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.

Our project aims to predict stock market trends using sentiment analysis of tweets. The sentiment of tweets related to a company will be analyzed and processed using the Naive Bayes algorithm. The processed data will then be used to train an XGBoost model to make predictions.

The Personalized Medicine Recommendation System leverages machine learning to help doctors prescribe optimal medications based on symptoms, history, and allergies. It enhances treatment accuracy, minimizes side effects, and improves patient safety with data-driven recommendations.

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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 trend prediction using sentiment analysis and machine learning

Stock Market Trend Prediction using Machine Learning and Sentiment Analysis

AI-based stock market trend prediction using sentiment analysis and Flask

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Stock Market Trend Prediction Using Machine Learning & News Sentiment Analysis. Webapp built using Django Python and Postgresql

This project involves Stock market prediction leveraging data analysis, statistical models, and machine learning to anticipate and forecast future movements in stock prices and market trends using sentiment analysis.

An AI-powered stock prediction system that analyzes market data, computes indicators, applies sentiment analysis, and forecasts trends using machine learning.

Stock trend prediction in the Brazilian market using deep learning with historical data and sentiment analysis from news and social media.

StockSense AI forecasts stock trends using LSTM networks & news sentiment analysis (NLP). It combines historical price data with market mood for smarter predictions.

A Stock Market Analysis and Prediction System built to analyze historical stock data, visualize trends, and predict future prices using machine learning techniques. The project integrates data processing, ML models, and sentiment analysis to deliver meaningful insights about market behavior.

Stock Sentiment Prediction is a machine learning project that predicts stock market sentiment by analyzing discussions on Reddit. Using a combination of web scraping, sentiment analysis, and classification models, this project aims to classify posts as positive or negative/neutral to provide insights into market trends.

Three-Way Stock Market Analysis using Machine Learning and LLMs – A project that combines deep learning (LSTM) for stock trend prediction, NLP-based sentiment analysis, and LLM-powered explanation generation with RAG to provide comprehensive insights into stock movements.

Built a stock market prediction system using machine learning and sentiment analysis. Analyzed news and social media to forecast trends. Showcased expertise in financial forecasting, data analysis, and ML for informed investment insights.

Forecast next-day stock prices using time series analysis and agentic AI. The system fetches recent news via API, analyzes sentiment using an AI market analyst, and combines it with technical trends to provide smart stock predictions.

Forecast next-day stock prices using time series analysis and agentic AI. The system fetches recent news via API, analyzes sentiment using an AI market analyst, and combines it with technical trends to provide smart stock predictions.