scattertext
pyRDF2Vec
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| scattertext | pyRDF2Vec | |
|---|---|---|
| 3 | 1 | |
| 2,197 | 240 | |
| - | 1.7% | |
| 4.7 | 2.9 | |
| about 2 months ago | 8 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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.
pyRDF2Vec
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[P] pyRDF2Vec 0.2.0 is out!
This release is packed with many new features and optimizations under the hood. An entire overview of what's new can be found in our CHANGELOG (https://github.com/IBCNServices/pyRDF2Vec/releases/tag/0.2.0). An overview of some major updates:
What are some alternatives?
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
text-summarizer - Python Framework for Extractive Text Summarization
KeyBERT - Minimal keyword extraction with BERT
gensim - Topic Modelling for Humans
stopwords-it - Italian stopwords collection
jRDF2Vec - A high-performance Java Implementation of RDF2Vec
word_cloud - A little word cloud generator in Python
hub - A library for transfer learning by reusing parts of TensorFlow models.
shifterator - Interpretable data visualizations for understanding how texts differ at the word level
YassQueenDB - Graph database library that allows you to store, analyze, and search through your data in a graph format. By using the Universal Sentence Encoder, it provides an efficient and semantic approach to handle text data. 📚🧠🚀
lit - The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.
magnitude - A fast, efficient universal vector embedding utility package.