Trump And Trudeau Twitter Analysis During COVID-19 Crisis Part 2

In our last post, we extract @realDonalTrump and @JustinTrudeau tweets, clean up the texts, and generate word clouds. In this article, we will build a Latent Dirichlet Allocation (LDA) model to study the topics of the hundreds of tweets posted by the two world leaders.

Topic Modeling

Topic modeling is an unsupervised machine learning technique which is widely used for discovering abstract topics of a collection of documents. It considers each document to be represented by several topics and each topic to be represented by a set of words that frequently appear together. For example, with a cluster of cloud, rain, wind, we can tell that the associated topic likely related to weather.

To Know What People Twitter About #Coronavirus In One Minute

Year 2020 is not off to a good start. The ongoing Coronavirus outbreak that originated in Wuhan, China has infected thousands of people worldwide and killed hundreds. Numbers are still rising everyday. With all the quarantine controls and vaccine development, hope this global epidemic will be soon under control.

When we are facing such a global challenge, we take our emotions and concerns to social media and share Coronavirus news with others. Since the outbreak, each day there are hundreds of thousands of tweets about Coronavirus. I decided to run analyses on Twitter feeds and see if I could generate some highlights.

Auto Generated Insights of 2019 HR Tech Conference Twitter – Part 2 (Topic Modeling)

In our last post, we extract #HRTechConf tweets, clean up the texts, and generate a word cloud that highlights some of the buzzwords from the conference. But, what are the tweets talking about? Without reviewing each of the 7,000 tweets, how could we find out the popular topics? Let’s explore and see if tweet topics could be auto detected by developing a Latent Dirichlet Allocation (LDA) model.

Feature Extraction

Tweets or any text must be converted to a vector of numbers – the dictionary that describes the occurrence of words in the text (or corpus). The technique we use is called Bag of Words, a simple method of extracting text features. Here are the steps.