In this third part of the series of posts about text mining of Twitter data, we'll focus on analyzing the datasets we’ve collected through the REST API from the previous lectures. In this article, we’ll see some descriptive statistics such as term frequencies, popular hashtags, and mentions ..etc, as well as some advanced NLP like topic modelling and sentiment analysis.
Twitter is a huge resource of raw data where you can explore and find interesting insights, millions of companies, politicians, journalists, and people are using this social media tool to interact with their audience and mining the data to learn more about trends and users.
This post is the first in the series dedicated to mining Twitter data using python. I focus in this first part on how to collect data from Twitter REST Search API using python to build our dataset, in the following parts I’ll show how to process the data an analyze it.