With recent advancements in the field of artificial intelligence, there are numerous natural language processing (NLP) approaches to analyze open-ended text data. To date, Topic Modeling has been one of the popular algorithms to make sense of these data. However, the standard output from this algorithm does not always satisfy the curiosity of one’s stakeholders. This blog explores 3 new ideas to apply in topic modeling that enhance interpretability of results, driving the potential for new insights from one’s data.

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