mtminepy 0.1.0

MTMinePy - A Multilingual Text Mining Platform for Academic Research

MTMinePy - Multilingual Text Miner with Python

MTMinePy is a Python-based academic text mining platform inspired by MTMineR. It is a comprehensive Flask web application designed for powerful text mining and analysis, supporting interactive visualization and advanced modeling.

Key Features

• Advanced NLP: Integrated with jieba, HanLP, LTP, Spacy, and NLTK.
• Multilingual: Native support for 10+ languages including Chinese, English, Japanese, and more.
• Interactive Visualization: Powered by ECharts, supporting responsive force-directed networks, dynamic word clouds, and interactive scatter plots.
• Academic Metric Analysis: Supports advanced distance functions (Hsim, Close, Esim) and high-end visualization.
• Advanced Modeling: Comprehensive suite of Unsupervised (Clustering, Topic Modeling) and Supervised learning algorithms.

Screenshots

Chinese Analysis

Co-occurrence Network	Word Cloud	Clustering

English Analysis

Co-occurrence Network	Word Cloud	Clustering

Installation

Install from PyPI (Recommended)

pip install mtminepy

To install with all optional NLP backends (Janome, spaCy, HanLP, LTP, UMAP, Boruta, etc.):

pip install mtminepy[full]

Install from source

git clone https://github.com/EasyCam/MTMinePy.git
cd MTMinePy
pip install -e .

Usage

Run from command line

After installation, run directly:

mtminepy

Access the dashboard at http://localhost:5000.

Command-line options

mtminepy --help
mtminepy --port 8080          # Custom port
mtminepy --host 127.0.0.1    # Bind to localhost only
mtminepy --debug              # Flask debug mode
mtminepy --version            # Show version

Run from Python

from mtminepy.app import create_app

app = create_app()
app.run(host='0.0.0.0', port=5000)

Advanced Capabilities

Modeling Algorithms

MTMinePy supports a wide range of standard machine learning algorithms for text analysis:

• Feature Engineering: TF-IDF, Bag of Words (CountVectorizer), N-gram support.
• Unsupervised Learning:
  • Topic Modeling: Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), STM (Structural Topic Model).
  • Clustering: K-Means, Agglomerative (Hierarchical), DBSCAN, Spectral Clustering.
  • Dimensionality Reduction: PCA, t-SNE, UMAP, Factor Analysis.
• Supervised Learning (Classification):
  • Support Vector Machines (SVM)
  • Random Forest
  • Linear Discriminant Analysis (LDA)
  • Quadratic Discriminant Analysis (QDA)
  • Logistic Regression (Elastic Net)

Mathematical Models (Distance & Similarity)

MTMinePy supports advanced metrics for academic research:

Advanced Custom Similarity Measures

1. Hsim (Yang Fengzhao, 2007) $$ Hsim(x_i, x_j) = \frac{1}{n} \sum_{k=1}^n \frac{1}{1+|x_{ik}-x_{jk}|} $$

2. Close (Shao Changsheng, et al., 2011) $$ Close(x_i, x_j) = \frac{1}{n} \sum_{k=1}^n e^{-|x_{ik}-x_{jk}|} $$

3. Esim (Wang Xiaoyang, et al., 2013) $$ Esim(x_{ik}, x_{jk}) = \frac{1}{n} \sum_{k=1}^d \omega_k e^{-\frac{|x_{ik}-x_{jk}|}{|x_{ik}-x_{jk}|+|x_{ik}+x_{jk}|/2}} $$