Topic modeling is an unsupervised technique as it is used to perform analysis on text data having no label attached to it. As the name suggests, it is used to discover a number of topics within the given sets of text like tweets, books, articles, and so on. Each topic consists of words where the order of the words does not matter. It performs automatic clustering of words that best describe a set of documents. It gives us insight into a number of issues or features users are talking about, as a group.
For example, let us say a company has launched the software in the market and receives a number of feedback regarding various product features within a specified time period. Now, rather than going through each review one by one, if we apply topic modeling, we will come to know how users have perceived the various features of the product very quickly. It is one of the essential techniques to perform text analysis on unstructured data. After performing topic modeling, we can even perform topic classification to predict under which topic the upcoming reviews fall. There are various techniques to perform topic modeling, among which LDA is considered to be the most effective one.
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