In this case study, we will demonstrate how you can use text network analysis to analyze EU grant opportunities. We will demonstrate how you can use the graph to identify the most relevant topics in the existing grant proposals. We will also show how you can use network visualization to identify the structural gaps within.
Johnny Peng took the Netflix movie descriptions per genre and analyzed the recurrent themes in the movies’ descriptions using InfraNodus. It turned out that every genre has its preferred topics and some of them reveal an implicit bias present within the cinema.
When we learn a new language, context is very important. Text network graphs can be used to help students learn how to use the new words and phrases in context, by making the new connections between the disjointed parts of the graph.
How to improve sentiment analysis using text network visualization that helps you see not only the most prominent topics but also the correlations between the responses of customers. Using the example of online surveys and Twitter data.
Word clouds are used to generate visual summaries of text, however, they have one major deficiency: they completely lose track of the context. As a result, there is a risk of getting false meaningless results and making the wrong assumptions about the underlying data. Text network analysis and visualization can solve this problem.
