topic-wizard 0.2.6

Pretty and opinionated topic model visualization in Python.

topicwizard

Pretty and opinionated topic model visualization in Python.

https://user-images.githubusercontent.com/13087737/234209888-0d20ede9-2ea1-4d6e-b69b-71b863287cc9.mp4

New in version 0.2.5 🌟 🌟

• Compatiblity with Gensim topic models 💥
• Compatibility with BERTopic(experimental 🧪)
• Topic name inference 🧠

Features

• Investigate complex relations between topics, words and documents
• Highly interactive
• Automatically infer topic names
• Name topics manually
• Pretty :art:
• Intuitive :cow:
• Clean API :candy:
• Sklearn, Gensim and BERTopic compatible :nut_and_bolt:
• Easy deployment :earth_africa:

Installation

Install from PyPI:

pip install topic-wizard

Usage (documentation)

Step 1:

Train a scikit-learn compatible topic model. (If you want to use non-scikit-learn topic models, check compatibility)

from sklearn.decomposition import NMF
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.pipeline import make_pipeline

# Create topic pipeline
topic_pipeline = make_pipeline(
    CountVectorizer(),
    NMF(n_components=10),
)

# Then fit it on the given texts
topic_pipeline.fit(texts)

Step 2:

Visualize with topicwizard.

import topicwizard

# You can get automatically assigned topic labels, that you can change manually later
topic_names = topicwizard.infer_topic_names(pipeline=pipeline)

# Then you can visualize your results
topicwizard.visualize(pipeline=topic_pipeline, corpus=texts, topic_names=topic_names)

Step 3:

Investigate :eyes: .

a) Topics

b) Words

c) Documents