yake
scattertext
| yake | scattertext | |
|---|---|---|
| 5 | 3 | |
| 1,573 | 2,198 | |
| 0.7% | - | |
| 3.0 | 4.7 | |
| 4 months ago | about 2 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
yake
- Show HN: Whisper.cpp and YAKE to Analyse Voice Reflections [iOS]
-
Simplest keyword extractor
Personally I prefer using YAKE.
- What method should be used to tag specific texts, when the dataset is too small for training a model?
- Is there any YAKE (yet another keyword extractor) implementation in R? Unsupervised Approach for Automatic Keyword Extraction using Text Statistical Features.
-
Alternate approaches to TF-IDF?
You can look for usage here: https://github.com/LIAAD/yake and there is also a reference section with publications for more details of how this works. From what I remember, each keyphrase candidate is assigned an aggregated score based on various features: position in the text, casing, frequency, surrounding text frequency...
scattertext
-
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
-
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?
rake-nltk - Python implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK.
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
KeyBERT - Minimal keyword extraction with BERT
pke - Python Keyphrase Extraction module
stopwords-it - Italian stopwords collection
simple_keyword_clusterer - A simple machine learning package to cluster keywords in higher-level groups.
word_cloud - A little word cloud generator in Python
flashtext - Extract Keywords from sentence or Replace keywords in sentences.
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.
dutch-word-embeddings - Dutch word embeddings, trained on a large collection of Dutch social media messages and news/blog/forum posts.