NLP & Topic Modelling to Extract Complex Data

For every speaker, there is always a saying which goes “be watchful with words you use in your talk”, “you cannot have the same keys words for every speech”.

To make the speech interesting and to make it suit the context, the set of words used should be different and must be tweaked based on the audience. It’s very important for every speaker to be very watchful with the words he or she uses in a speech. Be it a faculty, a politician or a comedian or a film star one must be very cautious about the words during their speeches. While speaking people tend to get carried away, usage of certain inappropriate words can create awkward situations to the reputation of the speaker.

Tweaking speeches is the most difficult task and a data driven approach helps in identify the distribution of words used in a speech and the topics which a speaker wants to cover in a speech. Before every speech, the speaker reviews and finalizes different versions speech transcript. Each transcript is reviewed multiple times to arrive at the final speech for delivery.

While preparing the transcript for the speech the speaker lists out the ideas or the topics which is to be conveyed in each of the versions. Too many ideas or too less ideas makes the speech ineffective. This transcript is reviewed multiple times and changed are incorporated based on the need. 

Review of speech transcripts can be more comprehensive by following some of the NLP techniques like topic modelling. Topic modelling is a technique which extracts hidden topics from a group of documents. Here one document represents of a speech transcript. For each of the topic extracted, we will get the distribution of words and for each document we shall get the distribution of topics contained in these documents. This would help the speaker to review the usage of words used.

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