Analysis of Twitter Sentiment to discover correlations with Bitcoin and other cryptocurrencies

An experimental cryptocurrency trading system that combines AI-powered analysis with real-time market data and social sentiment monitoring. Features multiple microservices including market monitoring, social sentiment analysis, AI-driven trading signals, and automated trade execution. Built for fun.

An MCP server that delivers cryptocurrency sentiment analysis to AI agents.

A Real-Time Cryptocurrency Price and Twitter Sentiments Analysis

Algorithmic Trading of Cryptocurrencies using Sentiment Analysis and Machine Learning

Neural Networks for Sentiment Analysis in the Cryptocurrency Market

Time series forecasting using RNN, Twitter Sentiment Analysis and Turtle Trading Strategy applied on Cryptocurrency

Automated cryptocurrency analysis and reporting tool using Python. It monitors market trends, analyzes data from CoinPaprika and CryptoNews APIs, and generates weekly reports with insights. The script integrates sentiment analysis with GPT-4 and sends results via email, making it easy to track market movements.

LLM and NLP models in Cryptocurrency Sentiment Analysis: A Comparative Classification Study

Identify the software project, create business case, arrive at a problem statement. REQUIREMENT: Window XP, Internet, MS Office, etc. Problem Description: - 1. Introduction of AI and Machine Learning: - Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. Artificial intelligence (AI) brings the genuine human-to-machine interaction. Simply, Machine Learning is the algorithm that give computers the ability to learn from data and then make decisions and predictions, AI refers to idea where machines can execute tasks smartly. It is a faster process in learning the risk factors, and profitable opportunities. They have a feature of learning from their mistakes and experiences. When Machine learning is combined with Artificial Intelligence, it can be a large field to gather an immense amount of information and then rectify the errors and learn from further experiences, developing in a smarter, faster and accuracy handling technique. The main difference between Machine Learning and Artificial Intelligence is , If it is written in python then it is probably machine learning, If it is written in power point then it is artificial intelligence. As there are many existing projects that are implemented using AI and Machine Learning , And one of the project i.e., Bitcoin Price Prediction :- Bitcoin (₿ ) (founder - Satoshi Nakamoto , Ledger start: 3 January 2009 ) is a digital currency, a type of electronic money. It is decentralized advanced cash without a national bank or single chairman that can be sent from client to client on the shared Bitcoin arrange without middle people's requirement. Machine learning models can likely give us the insight we need to learn about the future of Cryptocurrency. It will not tell us the future but it might tell us the general trend and direction to expect the prices to move. These machine learning models predict the future of Bitcoin by coding them out in Python. Machine learning and AI-assisted trading have attracted growing interest for the past few years. this approach is to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. the application of machine learning algorithms to the cryptocurrency market has been limited so far to the analysis of Bitcoin prices, using random forests , Bayesian neural network , long short-term memory neural network , and other algorithms. 2. Applications/Scope of AI and Machine Learning :- a) Sentiment Analysis :- It is the classification of subjective opinions or emotions (positive, negative, and neutral) within text data using natural language processing. b) It is Characterized as a use of computerized reasoning where accessible data is utilized through calculations to process or help the handling of factual information. BITCOIN PRICE PREDICTION USING AI AND MACHINE LEARNING: - The main aim of this is to find the actual Bitcoin price in US dollars can be predicted. The chance to make a model equipped for anticipating digital currencies fundamentally Bitcoin. # It works the prediction by taking the coinMarkup cap. # CoinMarketCap provides with historical data for Bitcoin price changes, keep a record of all the transactions by recording the amount of coins in circulation and the volume of coins traded in the last 24-hours. # Quandl is used to filter the dataset by using the MAT Lab properties. 3. Problem statement: - Some AI and Machine Learning problem statements are: - a) Data Privacy and Security: Once a company has dug up the data, privacy and security is eye-catching aspect that needs to be taken care of. b) Data Scarcity: The data is a very important aspect of AI, and labeled data is used to train machines to learn and make predictions. c) Data acquisition: In the process of machine learning, a large amount of data is used in the process of training and learning. d) High error susceptibility: In the process of artificial intelligence and machine learning, the high amount of data is used. Some problem statements of Bitcoin Price Prediction using AI and Machine Learning: - a) Experimental Phase Risk: It is less experimental than other counterparts. In addition, relative to traditional assets, its level can be assessed as high because this asset is not intended for conservative investors. b) Technology Risks: There is a technological risk to other cryptocurrencies in the form of the potential appearance of a more advanced cryptocurrency. Investors may simply not notice the moment when their virtual assets lose their real value. c) Price Variability: The variability of the value of cryptocurrency are the large volumes of exchange trading, the integration of Bitcoin with various companies, legislative initiatives of regulatory bodies and many other, sometimes disregarded phenomena. d) Consumer Protection: The property of the irreversibility of transactions in itself has little effect on the risks of investing in Bitcoin as an asset. e) Price Fluctuation Prediction: Since many investors care more about whether the sudden rise or fall is worth following. Bitcoin price often fluctuates by more than 10% (or even more than 30%) at some times. f) Lacks Government Regulation: Regulators in traditional financial markets are basically missing in the field of cryptocurrencies. For instance, fake news frequently affects the decisions of individual investors. g) It is difficult to use large interval data (e.g., day-level, and month-level data) . h) The change time of mining difficulties is much longer. Moreover, do not consider the news information since it is hard to determine the authenticity of a news or predict the occurrence of emergencies.

A real-time cryptocurrency analysis platform that combines technical analysis, sentiment analysis, volume analysis, news trends, and AI-powered predictions to provide comprehensive market insights and trading recommendations.

A sentiment analysis approach using news articles to predict cryptocurrency price.

Sentiment Analysis and Trend Monitoring to Predict Cryptocurrency Market Movements

This repository has been created as part of the kaggleXBIPOC Mentorship Program. The aim of this project is to establish the sentiment association between the various cryptocurrencies like Bitcoin, Ether using real time twitter data and RoBERTa Model for classification .

Twitter Sentiment Analysis for the top 10 cryptocurrencies on CoinMarketCap

Monitor tool for stocks and cryptocurrencies that includes twitter sentiment analysis on key accounts

A free and simple cryptocurrency sentiment analysis API

Sentiment analysis on tweets analyzing any trade-able asset (Cryptocurrency) using Machine Learning and statistical classification model

Cryptocurrency price prediction based on LSTM and FinBERT sentiment analysis

Contains code used for my undergraduate dissertation research project centred around sentiment analysis of the Bitcoin cryptocurrency

Predicted the Price of the Cryptocurrency(Bitcoin) using the past time-series data, Twitter Sentiments(Polarity and Sensitivity), Currency's Fundamentals and Technical Indicators like RSI and SMA on LSTM. The Notebook contains the Exploratory data analysis(with important links) and the astounding result at the end of it

Welcome to the AI-Powered Trading Bot repository! This project is a comprehensive, high-performance trading bot that integrates machine learning, reinforcement learning, and sentiment analysis to make informed trading decisions across multiple markets, including stocks, forex, and cryptocurrencies.

A telegram bot that fetches multiple RSS cryptocurrency news feeds for sentiment analysis

A comprehensive Python tool for crypto project analysis Combines Twitter sentiment analysis, Telegram message extraction, token security checks, influencer metrics, and Raydium DEX interactions. Ideal for researchers and traders seeking holistic insights into cryptocurrency projects.

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Cryptocurrency automated trading bot using machine learning through sentiment and market analysis.

Crypto Price Predictor experimental project, a machine learning system designed to predict cryptocurrency price movements by combining real-time market data with news sentiment analysis.

The Real-Time Crypto Dashboard simplifies cryptocurrency analysis for beginners by providing real-time updates, candlestick charts, and key metrics like SMA, EMA, and volume trends. In addition to historical data and market sentiment analysis, it includes LSTM-based price predictions, offering insights into future price movements.

Cryptocurrency reddit sentiment analysis application.

Cryptocurrency bot for storing trade data from Binance along with sentiment analysis for coins on Twitter.