15 best Python Topic Modelling libraries in 2025

Find patterns or themes in large document sets, create links, pinpoint important subjects, implement popular algorithms like LSA/LSI/SVD, and Artificial Intelligence

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: 

gensim  

• Gensim is an open-source Python library designed to work with natural language processing.  
• Gensim allows you to represent documents as vectors in a high-dimensional space  
• This is useful for tasks like document clustering and retrieval.  

MUSE  

• MUSE is short for Multilingual Unsupervised and Supervised Embeddings.  
• Research project and toolkit developed by Facebook AI Research for training words.  
• MUSE supports both supervised and unsupervised methods for aligning word embeddings across languages.  

texthero  

• TextHero is a Python library for text preprocessing, representation, and visualization.  
• It simplifies common text processing tasks and allows users to operate on data.  
• Built on top of popular libraries like Pandas, SpaCy, and Scikit-learn.  

BERTopic  

• BERTopic is a Python library that leverages the BERT language model for modeling.  
• BERTopic uses pre-trained BERT models to generate contextual word embeddings.  
• BERTopic supports the creation of a hierarchical representation of topics.  

awesome-sentence-embedding  

• awesome-sentence-embedding is a Python library. It is typically used in Institutions, Learning, Education, and Artificial Intelligence.  
• awesome-sentence-embedding has no bugs, it has no vulnerabilities.  
• A curated list of pre-trained sentence and word embedding models.

scattertext  

• Scattertext provides scatter plots that visualize the term frequency of words.  
• The plots show the prevalence of terms in one category relative to another.  
• The library calculates association statistics, such as log odds ratio and significance.  

word2vec-api  

• Word2Vec is often implemented as part of larger NLP libraries or frameworks.  
• word2vec-api is a Python library typically used in Artificial Intelligence, Natural Language Processing.  
• It has built files available, and it has medium support.  

deep-siamese-text-similarity  

• deep-siamese-text-similarity is a Python library typically used in Artificial Intelligence, Machine Learning, etc.  
• Deep-siamese-text-similarity has no bugs, it has no vulnerabilities.  
• deep-siamese-text-similarity has a medium active ecosystem.  

nlp-journey  

• nlp-journey is a Python library. It is typically used in Institutions, Learning, Education, and Artificial Intelligence.  
• nlp-journey has no bugs, it has no vulnerabilities, and it has built files available.  
• It has a Permissive License, and it has a medium support.  

lda  

• Latent Dirichlet Allocation, a generative statistical model used for topic modeling.  
• It is a popular technique in NLP and ML for discovering topics.  
• LDA assumes that there are K topics in the entire corpus.  

contextualized-topic-models  

• contextualized-topic-models is a Python library typically used in Artificial Intelligence, Natural Language Processing.  
• contextualized-topic-models has no bugs, it has no vulnerabilities, it has built file available.  
• This approach combines the strengths of contextual embeddings with the interpretability of models.  

ETM  

• The Embedding Topic Model is a probabilistic topic modeling approach that incorporates distributes.  
• It Represent each document as a distribution over topics.  
• ETM is a Python library typically used in Artificial Intelligence, Topic Modeling applica

GuidedLDA  

• GuidedLDA is an extension of Latent Dirichlet Allocation (LDA), a popular modeling algorithm.  
• The algorithm incorporates the seed words as prior information during the topic modeling.  
• It adjusts the topic-word probabilities to align with the provided guidance.  

dynamic-nmf  

• Dynamic NMF extends traditional NMF to capture temporal patterns in data.  
• dynamic-nmf is a Python library typically used in Artificial Intelligence, Topic Modeling applications.  
• Dynamic NMF has applications in various domains, such as audio processing, video analysis.  

topics  

• Topics is a Python library typically used in Artificial Intelligence, Topic Modeling applications.  
• Topics has no bugs; it has no vulnerabilities.  
• It has a Permissive License, and it has low support.  

FAQ

1. What is topic modeling?  

Topic modeling, a NLP technique that identifies topics or themes. It helps discover hidden patterns, group similar documents, and extract meaningful insights.  

2. What is Latent Dirichlet Allocation (LDA)?  

LDA is a probabilistic model used for topic modeling. It assumes that each document in a collection is a mixture of topics and each word in the document.  

3. Can I use topic modeling for short texts like tweets?  

Yes, topic modeling applies to short texts like tweets. The brevity of tweets poses challenges, prompting consideration of alternatives like word embeddings.  

4. How to apply topic modeling to real-world scenarios?  

Topic modeling has various applications. These include content recommendation, document clustering, and sentiment analysis. It is widely used in industries like marketing, healthcare, and social media analysis.  

5. Are there Python packages for dynamic or temporal topic modeling?  

Yes, there are packages like gensim that support dynamic topic modeling. It allows the modeling of topic evolution over time in a collection of documents.