By means of the Topic Discovery analysis it is possible to understand the most discussed topics of some complex textual data. This allows also to capture the underlying justification to the sentiment and NPS evolutions over time, and to perform various types of cross-analysis.
The Wordcloud is usually a good starting point to discover the main trending topics people are talking about on the web. It shows hashtags, keywords or sentences with the size of each word indicating the volume of use relative to the other, and the color showing their “trendiness”.
In this research example, the two wordclouds show the main economic topic discussed by italian people during the period of Coronavirus.
The Topic Model, based on LDA (Latent Dirichlet Allocation) analysis, is another useful unsupervised method that helps to identify the interesting topics that underline the textual data.
The three different flower plots show three different topics italian people were talking about during the Coronavirus crisis.