Stock market analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis

This program goes thru reddit, finds the most mentioned tickers and uses Vader SentimentIntensityAnalyzer to calculate the ticker compound value.

Java port of Python NLTK Vader Sentiment Analyzer. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.

Tool for measurement of digital biomarkers from video or audio of an individual’s behavior.

JavaScript port of VADER sentiment analysis tool

This is our final year project. In this we are predicting election, results using Twitter Sentiment Analysis.

Application made for youtube content creators to know their reviews separately as positive and negative comments.

A machine learning end to end flask web app for sentiment analysis model created using Scikit-learn & VADER Sentiment.

📷 An app to scrap instagram posts and analyze data.

An overview of various quantitative techniques and trading strategies for predicting stock prices, based on historical data from YahooFinance.

Submission of an in-class NLP sentiment analysis competition held at Microsoft AI Singapore group. This submission entry explores the performance of both lexicon & machine-learning based models

stock market predictions using sentiment analysis, a deep learning project(data and news based on pakistani stock exchange and news(Dawn news))

A Real-Time Cryptocurrency Price and Twitter Sentiments Analysis

Sentiment analysis for tweets written in Portuguese-Brazil

A Discord sentiment analysis bot that encourages positivity and rewards server members for saying nice things :). Built using Node.js and Discord.js

Detects bots from a small subset of Twitter accounts and classifies them as positive, negative or neutral by the sentiment of their tweets.

Analyse sentiments of Instagram users based on their post captions

This is a trend trading indicator and alert that utilizes the Traders Dynamic Index (TDI), Price Action Channel (PAC) and Heikin Ashi candles.

I used Catboost for training a model on the numerical features of every YouTube video (e.g., the number of views, comments, likes, etc.) along with sentiment analysis of the video descriptions and comments using the VADER sentiment analysis model.

Reading the Twitterstream from the Twitter-API with Kafka and stream them into an Spark-Cluster to process it