zizhan / text-classifier

text-classifier is a toolkit for text classification. It was developed to facilitate the designing, comparing, and sharing of text classification models.

text-classifier

Text classifier. It can be applied to the fields of sentiment polarity analysis, text risk classification and so on, and it supports multiple classification algorithms.

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text-classifier s a python Open Source Toolkit for Chinese text categorization. The goal is to implement text categorization algorithm, so as to achieve the use in the generative environment. text-classifier has the characteristics of clear algorithm, high performance and customizable corpus.

text-classifier provides the following functions：

• Classifier

• LogisticRegression
• MultinomialNB
• KNN
• SVM
• RandomForest
• DecisionTreeClassifier
• Xgboost
• Neural Network

• Evaluate

• Precision
• Recall
• F1

• Test

• Chi-square test

While providing rich functions, text-classifier internal modules adhere to low coupling, model adherence to inert loading, dictionary publication, and easy to use.

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demo

http://www.borntowin.cn/product/sentiment_classify

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Usage

• text classifier

1. modify config.py
2. run segment.py -> train.py -> infer.py:

python train.py
python infer.py

Algorithm

• [done] LogisticRegression
• [done] Random Forest
• [done] Decision Tree
• [done] K-Nearest Neighbours
• [done] Naive bayes
• [done] Xgboost
• [done] Support Vector Machine(SVM)
• [done] MLP
• [done] Ensemble
• [done] Stack
• [done] CNN

Thanks

• SentimentPolarityAnalysis

Licence

• Apache Licence 2.0