qhchina 0.0.36

A Python package for NLP tasks related to Chinese text.

QHChina

Quantitative Humanities China Lab - A Python package for NLP tasks related to Chinese text analysis.

Features

• Collocation Analysis: Find significant word co-occurrences in text
• Corpus Comparison: Statistically compare different corpora
• Word Embeddings: Work with Word2Vec and other embedding models
• Text Classification: BERT-based classification and analysis
• Topic Modeling: Fast LDA implementation with Cython acceleration

Installation

pip install qhchina

Usage Examples

Topic Modeling with LDA

from qhchina.analytics import LDAGibbsSampler

# Each document is a list of tokens
documents = [
    ["word1", "word2", "word3"],
    ["word2", "word4", "word5"],
    # ...
]

# Initialize and train the model
lda = LDAGibbsSampler(
    n_topics=10,
    iterations=500
)
lda.fit(documents)

# Get top words for each topic
for i, topic in enumerate(lda.get_topic_words(10)):
    print(f"Topic {i}: {[word for word, _ in topic]}")

For more examples, see the module documentation.

Documentation

For complete API documentation and tutorials, visit: https://mcjkurz.github.io/qhchina/

License

This project is licensed under the MIT License - see the LICENSE file for details.