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

top 1% solution to toxic comment classification challenge on Kaggle.

Technical and sentiment analysis to predict the stock market with machine learning models based on historical time series data and news article sentiment collected using APIs and web scraping.

This script can tell you the sentiments of people regarding to any events happening in the world by analyzing tweets related to that event

This repository provides everything to get started with Python for Text Mining / Natural Language Processing (NLP)

🔍 This project is about searching the twitter for job opportunities using popular hashtags and applying sentiment analysis on this. #️⃣ 🐦

Arabic support for textblob

Inhibits commits with bad messages from getting merged

Sentiment Analysis Project using Natural Language Processing (NLP) Techniques

A Python programming course at Omnia, Fall 2024

Basic script to retrieve and perform Sentiment Analysis on Facebook Posts.

This is a machine learning based sentiment analysis web application using python's nltk library and deployed using flask api on Heroku server.

Simple example of using TextBlob to perform sentiment analysis

Analisis sentimen masyarakat Indonesia terhadap kebijakan pemerintah mengenai vaksin COVID-19.

The objective of this project is to utilize the IMDB data set to generate Meaningful and Interesting Insights and then create a movie rating model based on average IMDB ratings and a sentiment analysis score of user tweets. And also to create an accurate Machine Learning model to predict average movie ratings based on some key features.

Application made with Node.js and Python.

A scientific benchmark and comparison of the performance of sentiment analysis models in NLP on small to medium datasets

Developed a hybrid-filtering personalized news articles recommendation system which can suggest articles from popular news service providers based on reading history of twitter users who share similar interests (Collaborative filtering) and content similarity of the article and user’s tweets (Content-based filtering)

Perform sentiment analysis on tweets using NLTK and TextBlob!

Introducing Natural Language Processing (NLP) with jupyter notebooks.