15 best Python Topic Modelling libraries in 2023

Find patterns or themes in large document sets, create links, pinpoint important subjects, implement popular algorithms like LSA/LSI/SVD, and more for your Artificial Intelligence, Topic Modeling, Bert, Neural Network, Transformer, and NLP applications. Topic modeling is a method for locating hidden subjects in vast amounts of text. Extensive collections of unstructured text bodies can be organized and understood using topic models. Topic models have been used to find instructional structures in data, including genetic information, pictures, and networks, since they were first created as a text-mining technique. The method falls under the category of an unsupervised machine learning algorithm. Latent Dirichlet Allocation (LDA) is the algorithm's name, a component of Python's Gensim module. 

Topic modeling is applied to several tasks, including document segmentation, classification, and summarization. Social networks, population genetics, and computer vision are some of the most novel applications. Topic modeling aids in query expansion in information retrieval. It also customizes search results or provides recommendations by associating user preferences with topics. 

Some key features of the Python Topic Modelling libraries are intuitive interfaces, the ease with which you can plug in your input corpus or datastream, distributed computing, state-of-the-art multilingual word embeddings, large-scale, high-quality bilingual dictionaries for training and evaluation, etc. 

Check out the below list to find the best Python topic modeling libraries for your application: