nmf-standalone 0.1.7

A standalone NMF topic modeling tool for Turkish and English texts

NMF Standalone

A comprehensive topic modeling library for Turkish and English texts using Non-negative Matrix Factorization (NMF).

Features

• Multi-language Support: Native support for Turkish and English text processing
• Advanced NMF Algorithms: Standard NMF and Orthogonal Projective NMF (OPNMF) variants
• Modern Tokenization: BPE and WordPiece tokenizers for Turkish, traditional preprocessing for English
• Comprehensive Preprocessing: Language-specific text cleaning, emoji processing, and normalization
• Rich Visualizations: Word clouds, topic distribution plots, and co-occurrence heatmaps
• Multiple Export Formats: Excel reports, JSON results, and database storage
• Coherence Evaluation: Built-in topic coherence scoring for model evaluation
• CLI and Python API: Both command-line interface and programmatic access

Installation

pip install nmf-standalone

For visualization features:

pip install nmf-standalone[visualization]

Quick Start

Command Line Interface

# Analyze Turkish app reviews with 5 topics
nmf-standalone analyze reviews.csv --column REVIEW --language TR --topics 5 --wordclouds

# Analyze English documents with lemmatization
nmf-standalone analyze docs.xlsx --column text --language EN --topics 10 --lemmatize --excel

# Use BPE tokenizer for Turkish text
nmf-standalone analyze data.csv --column content --language TR --tokenizer bpe --topics 7

Python API

from nmf_standalone import run_topic_analysis

# Simple analysis
result = run_topic_analysis(
    "data.csv",
    column="text_column", 
    language="TR",
    topics=5,
    generate_wordclouds=True
)

# Access results
topics = result['topic_word_scores']
for topic_id, words in topics.items():
    print(f"Topic {topic_id}: {', '.join([word for word, score in words[:5]])}")

# Advanced configuration
result = run_topic_analysis(
    "reviews.csv",
    column="review_text",
    language="TR", 
    topics=7,
    nmf_method="opnmf",  # Use projective NMF
    tokenizer_type="wordpiece",
    words_per_topic=20,
    export_excel=True,
    topic_distribution=True
)

Supported File Formats

• CSV files: Automatic delimiter detection, UTF-8 encoding
• Excel files: .xlsx and .xls formats
• Text columns: Any column containing text data for analysis

Language Support

Turkish

• Preprocessing: Advanced text cleaning, Turkish-specific normalization
• Tokenization: BPE (Byte-Pair Encoding) and WordPiece tokenizers
• Emoji Processing: Intelligent emoji-to-text conversion
• TF-IDF: BM25 and traditional TF-IDF with Turkish language adaptations

English

• Preprocessing: Standard NLP preprocessing with optional lemmatization
• Tokenization: Traditional word-based tokenization
• Lemmatization: NLTK-based lemmatization support
• TF-IDF: Classical TF-IDF with multiple weighting schemes

Algorithm Options

NMF Methods

• Standard NMF (nmf): Classical non-negative matrix factorization
• Orthogonal Projective NMF (opnmf): Enhanced variant for better topic separation

Tokenization (Turkish)

• BPE (bpe): Byte-Pair Encoding for subword tokenization
• WordPiece (wordpiece): Google's WordPiece algorithm

Output Formats

Generated Files

• Word Clouds: PNG images for each topic showing prominent words
• Excel Reports: Detailed topic-word matrices with scores
• Topic Distribution: Plots showing document-topic relationships
• JSON Results: Machine-readable topic and document data
• Database Storage: SQLite databases for persistent storage

Directory Structure

Output/
└── {dataset_name}/
    ├── {dataset}_topics.xlsx           # Excel report
    ├── {dataset}_coherence_scores.json # Model evaluation
    ├── {dataset}_document_dist.png     # Topic distribution
    ├── top_docs_{dataset}.json         # Top documents per topic
    └── wordclouds/                     # Word cloud images
        ├── Topic_00.png
        ├── Topic_01.png
        └── ...

Configuration Options

Parameter	Type	Default	Description
topics	int	5	Number of topics to extract
words_per_topic	int	15	Top words to display per topic
language	str	"TR"	Language code (TR/EN)
nmf_method	str	"nmf"	Algorithm variant (nmf/opnmf)
tokenizer_type	str	"bpe"	Tokenizer for Turkish (bpe/wordpiece)
lemmatize	bool	False	Apply lemmatization (English only)
generate_wordclouds	bool	True	Create word cloud visualizations
export_excel	bool	True	Export Excel reports
topic_distribution	bool	True	Generate distribution plots

Requirements

• Python ≥ 3.8
• NumPy ≥ 1.24.0
• Pandas ≥ 2.0.0
• scikit-learn ≥ 1.3.0
• NLTK ≥ 3.8.0
• gensim ≥ 4.3.0
• tokenizers ≥ 0.19.0

Examples

Turkish App Review Analysis

result = run_topic_analysis(
    "app_reviews.csv",
    column="review_text",
    language="TR",
    topics=8,
    tokenizer_type="bpe",
    generate_wordclouds=True,
    export_excel=True
)

English Document Classification

result = run_topic_analysis(
    "documents.xlsx", 
    column="content",
    language="EN",
    topics=12,
    lemmatize=True,
    nmf_method="opnmf",
    words_per_topic=25
)

Medical Text Analysis

result = run_topic_analysis(
    "medical_notes.csv",
    column="impression", 
    language="EN",
    topics=15,
    lemmatize=True,
    generate_wordclouds=True,
    topic_distribution=True
)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

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

Citation

If you use this library in your research, please cite:

@software{nmf_standalone,
  author = {Emir Karayagiz},
  title = {NMF Standalone: Topic Modeling for Turkish and English Texts},
  url = {https://github.com/emirkarayagiz/nmf-standalone},
  version = {0.1.0},
  year = {2024}
}