rwalk/gsdmm
GSDMM: Short text clustering
This tool helps organize short pieces of text, like social media posts, product reviews, or customer feedback, into meaningful categories. You provide a collection of these short texts, and it automatically groups similar ones together, even if you don't know how many categories there should be. This is ideal for analysts, marketers, or researchers who need to quickly understand themes within large volumes of concise written content.
357 stars. No commits in the last 6 months.
Use this if you have a dataset of many short text snippets and need to automatically discover underlying themes or clusters without defining categories beforehand.
Not ideal if your texts are long documents or if you already have predefined categories you want to assign texts to.
Stars
357
Forks
92
Language
Python
License
MIT
Category
Last pushed
Dec 28, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/rwalk/gsdmm"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
MIND-Lab/OCTIS
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models...
i-dot-ai/themefinder
A topic modelling Python package for analysing one-to-many question-answer data.
andifunke/topic-labeling
The project proposes a framework to apply topic models on a text-corpus and eventually topic...
bab2min/tomotopy
Python package of Tomoto, the Topic Modeling Tool
bobxwu/TopMost
A Topic Modeling System Toolkit (ACL 2024 Demo)