Using Topic Modelling & Twitter scraping to extract Voice of Customer after the Clicks Group racially offensive advertisement debacle.

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image by Erin Gaets, The Spectator

On Friday, 4 September, Twitter reacted to the Clicks Group, one of South Africa’s largest healthcare retailer after they released an online advert depicting a black woman’s hair described as ‘frizzy and dull’, and a white woman’s hair described as ‘normal’ as part of an ad campaign with American hair care brand , TRESemmé.

In this post I scrape Twitter data related to the debacle and use a Natural Language Processing algorithm, the latent Dirichlet allocation (LDA) to unveil some the major topics which emerge from the twitter discourse since the event. …

Using an LDA Topic Modelling algorithm and Twitter Scraper to draw social media insights from the 2020 explosions in Lebanon.

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Pics: Talal Traboulsi/Reuters/TR/AFP/Getty

On the afternoon of 4 August 2020, Twitter was ablaze with people from all over the globe sharing their sentiments on the two explosions which occurred at the port of the city of Beirut, the capital of Lebanon leaving at least 135 people dead and some 5,000 more are injured.

In this post, we are going to be looking at some ways in which we can extract twitter text linked to the event and apply a popular topic modelling algorithm, LDA ( Latent Dirichlet Allocation) to best extract insights from twitter users’ responses to the catastrophe. …


Julian Nkuna

Applied Data Scientist, Researcher & Illustrator.

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