NLP Projects for Topic Modeling and Document Clustering

 Topic modelling and document clustering are two of the most popular natural language processing projects. Topic modelling is a process of automatically identifying the topics present in a text, while document clustering is a method of grouping similar documents together. Both of these techniques are useful for analyzing large amounts of text data and extracting meaningful insights.

Topic modelling can be used to identify topics in a given document or a corpus of documents. This technique is useful for understanding the overall structure of a text and for uncovering unique topics that may be present. It can also be used to find topics that are related to each other and to identify the relationships between them. Common algorithms used for topic modelling include Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF).

Document clustering is a process of automatically grouping documents together based on their content. This technique is useful for finding similar documents and for organizing text data into clusters. Common algorithms used for document clustering include K-means clustering and hierarchical clustering.

Both topic modelling and document clustering are powerful techniques for analyzing large amounts of text data. They can be used together to uncover meaningful patterns and relationships between documents. For example, by combining topic modelling and document clustering, it is possible to identify topics that are related to each other and to group similar documents together. This can be useful for tasks such as text summarization, social media analysis, and sentiment analysis.