twitter_text_analysis
The goal of this project is to perform keyword network analysis and word frequency analysis to draw insights from data.
In the first task we build an adjacency matrix for the keywords in an article and convert the adjacency matrix to a weighted network. After that, we compute the strength and degree of the network and show the top keywords by strength and degree. Then, we show the top keyword pairs by finding top weighted edges. Finally, by plotting the average strength and degree, we analyze the network.
In the second task we analyze twitter data of Elon Musk between 2017 and 2021 to find the top words and pairs. We compute word frequencies for each year with and without the stop words and display the top words in each year. After that we visualize term frequency for each year. Then, we use Zipf’s law and plot a log-log plot of world frequency by rank. Finally, we creat bigram networks for each year to show how the words are related to eachother in each year.