embedded-topic-model
scattertext
embedded-topic-model | scattertext | |
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1 | 3 | |
82 | 2,198 | |
- | - | |
7.3 | 4.7 | |
8 months ago | about 2 months ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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embedded-topic-model
scattertext
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Clustering of text - Where to start?
If what you want is to determine how similar two categories are, or to learn something about the structure or words that compose those categories, you might consider word shift graphs or Scattertext.
- [Data] Principali parole degli ultimi (circa) 200 post sul sub
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Alternate approaches to TF-IDF?
Other suggestions: Take a look at Scattertext. Compare keywords to the problem of aspect extraction. I think an underutilized way to look at textual data when you have a single group of interest is the word-frequency-based odds ratio.
What are some alternatives?
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
KeyBERT - Minimal keyword extraction with BERT
stopwords-it - Italian stopwords collection
word_cloud - A little word cloud generator in Python
shifterator - Interpretable data visualizations for understanding how texts differ at the word level
lit - The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.
yake - Single-document unsupervised keyword extraction
dutch-word-embeddings - Dutch word embeddings, trained on a large collection of Dutch social media messages and news/blog/forum posts.
faiss - A library for efficient similarity search and clustering of dense vectors.
texthero - Text preprocessing, representation and visualization from zero to hero.
pyRDF2Vec - 🐍 Python Implementation and Extension of RDF2Vec
gensim - Topic Modelling for Humans