What are topic models that are used in Gensim

In this recipe, you will learn what is a topic, what role do topic models play in text processing, and the objectives of topic models in a very detailed manner.

What are Topic Models in Gensim?

A topic model is a probabilistic model that stores information about the themes in our text. However, two crucial questions arise in this situation:

First and foremost, what is a topic?

As the word implies, the fundamental ideas or themes reflected in our literature are called topics. For instance, a corpus of newspaper articles might include topics such as finance, weather, politics, sports, news from various states, etc.

Second, what role do topic models play in text processing?

We already know that we can use words to do information retrieval and searching strategies to find similarities in text. However, we can now search and organize our text files based on subjects rather than words.

Topics can be thought of as the probability distribution of words in this way. As a result, we can use topic models to help us.

Topic Models' Objectives

As previously said, topic modeling is concerned with underlying ideas and themes. Its key objectives are as follows:
For text summarization, topic models can be employed.

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They can help you organize your documents. For example, topic modeling can manage news articles into an organized/ linked segment, such as organizing all news stories on cricket.

They have the potential to improve search results. We may use topic models to reveal documents that have a mix of various keywords but are about the same idea in response to a search query.

For marketing purposes, the concept of recommendations is quite valuable. Various online retail websites, news websites, and other websites use it. Topic models assist in making recommendations for things to buy, read next, and so on. They accomplish this by compiling a list of materials with a common theme.

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