Classwork_0603

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Topic Modelling:
Topic modeling is a machine learning technique that automatically analyzes text data to determine cluster words for a set of documents. This is known as ‘unsupervised’ machine learning because it doesn’t require a predefined list of tags or training data that’s been previously classified by humans.Topic modeling is a technique in natural language processing (NLP) used to automatically discover the hidden thematic structure of a collection of text documents. It's essentially a way to uncover the main ideas or subjects discussed within a large set of text data
Here's a breakdown of key aspects of topic modeling:

• Unsupervised Learning: Topic modeling is unsupervised, meaning it doesn't require pre-labeled data. The algorithms analyze the text itself to identify patterns and emerging topics.
• Statistical Modeling: It uses statistical methods to identify thematic clusters of words that frequently appear together across documents. These clusters represent the underlying topics.
  Applications:

Topic modeling has a wide range of applications, including:

• Document organization and summarization: By identifying the main topics in a document collection, topic modeling can help categorize and organize documents. It can also be used to automatically generate summaries of large amounts of text data.
• Customer research: Businesses can use topic modeling to analyze customer reviews, social media conversations, or survey responses to understand customer sentiment and identify emerging trends.
  Scientific literature review: Researchers can use topic modeling to identify key themes and research areas within a large body of scientific literature.

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