Topic Modeling

Topic Modeling

Topic modeling is an unsupervised NLP method that examines how words and phases co-occur in the documents to automatically identify groups or clusters of words that best characterize these documents. These sets of words often represent a theme or topic.

Topic Modelling begins with an assumption that every document is characterized by a fixed number of topics. The model analyzes the underlying structure of the data and attempts to find a group of words that best fit your corpus.

For example, words like "Customer Service," "Shipping Experience," and "Product Quality" are often found in customer reviews, whereas words like "Machine Learning" and "Artificial Intelligence" are commonly found in data science topics.

In Rubiscape, you can use advanced techniques for topic modeling, such as Latent Dirichlet Allocation (LDA).

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