Find more libaries on Python Topic Modelling

Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community. All algorithms are memory-independent w.r.t. the corpus size (can process input larger than RAM, streamed, out-of-core), Intuitive interfaces  easy to plug in your own input corpus/datastream (trivial streaming API) easy to extend with other Vector Space algorithms (trivial transformation API) Efficient multicore implementations of popular algorithms, such as online Latent Semantic Analysis (LSA/LSI/SVD), Latent Dirichlet Allocation (LDA), Random Projections (RP), Hierarchical Dirichlet Process (HDP) or word2vec deep learning. Distributed computing: can run Latent Semantic Analysis and Latent Dirichlet Allocation on a cluster of computers. Extensive documentation and Jupyter Notebook tutorials. 

A library for Multilingual Unsupervised or Supervised word Embeddings

MUSE is a Python library for multilingual word embeddings, whose goal is to provide the community with:. We include two methods, one supervised that uses a bilingual dictionary or identical character strings, and one unsupervised that does not use any parallel data (see Word Translation without Parallel Data for more details). state-of-the-art multilingual word embeddings (fastText embeddings aligned in a common space) large-scale high-quality bilingual dictionaries for training and evaluation 

Text preprocessing, representation and visualization from zero to hero.

Text preprocessing, representation and visualization from zero to hero. From zero to hero • Installation • Getting Started • Examples • API • FAQ • Contributions. Texthero's community is growing fast. Texthero though is still in a beta version; soon, a faster and better version will be released and it will bring some major changes. For instance, to give a more granular control over the pipeline, starting from the next version on, all preprocessing functions will require as argument an already tokenized text. This will be a major change. Once released the stable version (Texthero 2.0), backward compatibility will be respected. Until this point, backward compatibility will be present but it will be weaker. 

Leveraging BERT and c-TF-IDF to create easily interpretable topics. 

For quick access to common functions, here is an overview of BERTopic's main methods:. | Method | Code | |-----------------------|---| | Fit the model | .fit(docs) | | Fit the model and predict documents | .fit_transform(docs) | | Predict new documents | .transform([new_doc]) | | Access single topic | .get_topic(topic=12) | | Access all topics | .get_topics() | | Get topic freq | .get_topic_freq() | | Get all topic information| .get_topic_info() | | Get representative docs per topic | .get_representative_docs() | | Get topics per class | .topics_per_class(docs, topics, classes) | | Dynamic Topic Modeling | .topics_over_time(docs, topics, timestamps) | | Update topic representation | .update_topics(docs, topics, n_gram_range=(1, 3)) | | Reduce nr of topics | .reduce_topics(docs, topics, nr_topics=30) | | Find topics | .find_topics("vehicle") | | Save model | .save("my_model") | | Load model | BERTopic.load("my_model") | | Get parameters | .get_params() |. 

 A curated list of pretrained sentence and word embedding models

Beautiful visualizations of how language differs among document types.

This is a tool that's intended for visualizing what words and phrases are more characteristic of a category than others. Consider the example at the top of the page. Looking at this seem overwhelming. In fact, it's a relatively simple visualization of word use during the 2012 political convention. Each dot corresponds to a word or phrase mentioned by Republicans or Democrats during their conventions. The closer a dot is to the top of the plot, the more frequently it was used by Democrats. The further right a dot, the more that word or phrase was used by Republicans. Words frequently used by both parties, like "of" and "the" and even "Mitt" tend to occur in the upper-right-hand corner. Although very low frequency words have been hidden to preserve computing resources, a word that neither party used, like "giraffe" would be in the bottom-left-hand corner. The interesting things happen close to the upper-left and lower-right corners. In the upper-left corner, words like "auto" (as in auto bailout) and "millionaires" are frequently used by Democrats but infrequently or never used by Republicans. Likewise, terms frequently used by Republicans and infrequently by Democrats occupy the bottom-right corner. These include "big government" and "olympics", referring to the Salt Lake City Olympics in which Gov. Romney was involved. Terms are colored by their association. Those that are more associated with Democrats are blue, and those more associated with Republicans red. Terms that are most characteristic of the both sets of documents are displayed on the far-right of the visualization. The inspiration for this visualization came from Dataclysm (Rudder, 2014). Scattertext is designed to help you build these graphs and efficiently label points on them. The documentation (including this readme) is a work in progress. Please see the tutorial below as well as the PyData 2017 Tutorial. Poking around the code and tests should give you a good idea of how things work. The library covers some novel and effective term-importance formulas, including Scaled F-Score. 

Simple web service providing a word embedding model

Tensorflow based implementation of deep siamese LSTM network to capture phrase/sentence similarity using character/word embeddings

Documents, papers and codes related to  Natural Language Processing, including Topic Model, Word Embedding, Named Entity Recognition, Text Classificatin, Text Generation, Text Similarity, Machine Translation)，etc. All codes are implemented intensorflow 2.0.

Topic modeling with latent Dirichlet allocation using Gibbs sampling

A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021.

This is code that accompanies the paper titled "Topic Modeling in Embedding Spaces" by Adji B. Dieng, Francisco J. R. Ruiz, and David M. Blei. (Arxiv link: https://arxiv.org/abs/1907.04907). ETM defines words and topics in the same embedding space. The likelihood of a word under ETM is a Categorical whose natural parameter is given by the dot product between the word embedding and its assigned topic's embedding. ETM is a document model that learns interpretable topics and word embeddings and is robust to large vocabularies that include rare words and stop words. 

semi supervised guided topic model with custom guidedLDA

Dynamic Topic Modeling via Non-negative Matrix Factorization

Standard topic modeling approaches assume the order of documents does not matter, making them unsuitable for time-stamped corpora. In contrast, dynamic topic modeling approaches track how language changes and topics evolve over time. We have developed a two-level approach for dynamic topic modeling via Non-negative Matrix Factorization (NMF), which links together topics identified in snapshots of text sources appearing over time. Greene, Derek, and James P. Cross. "Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach." Political Analysis 25.1 (2017): 77-94. [PDF] [BibTeX] [Preprint] 

Please read more about this repository on my blog http://www.vladsandulescu.com/topic-prediction-lda-user-reviews/. 