dutch-word-embeddings
scattertext
| dutch-word-embeddings | scattertext | |
|---|---|---|
| 1 | 3 | |
| 41 | 2,198 | |
| - | - | |
| 1.8 | 4.7 | |
| about 2 years ago | about 2 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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dutch-word-embeddings
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?
semantle
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
AnnA_Anki_neuronal_Appendix - Using machine learning on your anki collection to enhance the scheduling via semantic clustering and semantic similarity
KeyBERT - Minimal keyword extraction with BERT
gensim - Topic Modelling for Humans
stopwords-it - Italian stopwords collection
flashtext - Extract Keywords from sentence or Replace keywords in sentences.
word_cloud - A little word cloud generator in Python
magnitude - A fast, efficient universal vector embedding utility package.
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