Final project for "Natural Language Processing" course at University of Tartu. December 2017.
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Updated
Dec 16, 2018 - Jupyter Notebook
Final project for "Natural Language Processing" course at University of Tartu. December 2017.
Data analysis platform that guides you in the creation of digital content and facilitates the development of marketing strategies.
Determining the correlation between the market value of company respect to the public opinion of that company. And to answer the question that "Does tweet volume have any effect on stock market trading volume?"
Pequeña introducción a la captura de tweets y algo de data mining usando R
En esta práctica se empaqueta y distribuye una aplicación Python que descarga y analiza tweets en función de puntuaciones de sentimiento. Los resultados del análisis se guardan en una base de datos MongoDB, y la información se muestra en la web.
Social Media Sentiment Analysis
This project aims to built, test and select a Natural Language Processing model that has optimal performance to predict cyberbullying type of tweets from an account
Text mining using tweets of three politicians from RJ.
Implementation of the method TSTE for detecting popular topics in tweet dataset. Models topic as a set of semantically connected words using topic graph. Based on paper M. Cataldi, L. Di Caro i C. Schifanella, „Emerging Topic Detection on Twitter based on Temporal and Social Terms Evaluation”.
A machine-learning approach to predicting market fluctuations based on tweet classification in social media articles
Trump Tweets Sentiment Analysis. Third Assignment for Data Analytics course @unimib18/19.
In this project, I applied sentiment analysis using a statistical machine learning model to capture the correlation between the tweets extracted from Twitter and stock’s price market movements. My exploration sought to answer if daily stock prices behave in response to a positive, neutral, or negative sentiment scoring of respective tweets
Sentiment Tweet Analysis with Python
Capstone project of CodingNomads' online Python Programming bootcamp. Over 100k tweets were mined using tweepy (Twitter API), stored using SQLAlchemy and finally analyzed.
A sentiment analysis tool for tweets
This repo is for a Supervised Classification Machine Learning project that attempts to classify tweets made by Donald Trump and Justin Trudeau
Pipeline used to analyze tweets using text mining and graph mining approaches
Implementation of the method Trend Miner for detecting popular topics in tweet dataset. Models topic as a set of semantically connected words using word cooccurrences. Based on paper N. Pervin, F. Fang, A. Datta, K. Dutta i D. Vandermeer, „Fast, scalable, and context-sensitive detection of trending topics in microblog post streams”.
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