Data homogeneity dependent topic modeling for information retrieval

Different topic modeling techniques have been applied over the years to categorize and make sense of large volumes of unstructured textual data. Our observation shows that there is not one single technique that works well for all domains or for a general use case. ​

In this study, we hypothesize that the performance of these algorithms depends on the variation and heterogeneity of topics mentioned in free text and aim to investigate this effect.