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> [![arXiv](https://img.shields.io/badge/arXiv-1904.08067-red.svg?style=flat)](https://arxiv.org/abs/1904.08067)
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> ![ansicolortags](https://img.shields.io/pypi/l/ansicolortags.svg%0A%20%20%20:target:%20https://github.com/kk7nc/Text_Classification/blob/master/LICENSE)
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> ![contributors](https://img.shields.io/github/contributors/kk7nc/Text_Classification.svg%0A%20%20%20:target:%20https://github.com/kk7nc/Text_Classification/graphs/contributors)
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> ![twitter](https://img.shields.io/twitter/url/http/shields.io.svg?style=social%0A%20:target:%20https://twitter.com/intent/tweet?text=Text%20Classification%20Algorithms:%20A%20Survey%0aGitHub:&url=https://github.com/kk7nc/Text_Classification&hashtags=Text_Classification,classification,MachineLearning,Categorization,NLP,NATURAL,LANGUAGE,PROCESSING)
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> [![twitter](https://img.shields.io/twitter/url/http/shields.io.svg)](https://twitter.com/intent/tweet?text=Text%20Classification%20Algorithms:%20A%20Survey%0aGitHub:&url=https://github.com/kk7nc/Text_Classification&hashtags=Text_Classification,classification,MachineLearning,Categorization,NLP,NATURAL,LANGUAGE,PROCESSING)
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![Referenced paper : [Text Classification Algorithms: A
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Survey](https://arxiv.org/abs/1904.08067)](docs/pic/WordArt.png)
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Referenced paper : [Text Classification Algorithms: A
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Survey](https://arxiv.org/abs/1904.08067)]
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![pic](../docs/pic/WordArt.png)
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Table of Contents
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=================
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Introduction
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------------
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![](docs/pic/Overview.png)
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![](../docs/pic/Overview.png)
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Text and Document Feature Extraction
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------------------------------------
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#### Global Vectors for Word Representation (GloVe)
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![image](/docs/pic/Glove.PNG)
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![image](../docs/pic/Glove.PNG)
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An implementation of the GloVe model for learning word representations
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is provided, and describe how to download web-dataset vectors or train
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For \#3, use `BidirectionalLanguageModel` to write all the intermediate
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layers to a file.
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![](docs/pic/ngram_cnn_highway_1.png)
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![](../docs/pic/ngram_cnn_highway_1.png)
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Architecture of the language model applied to an example sentence
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[Reference: [arXiv paper](https://arxiv.org/pdf/1508.06615.pdf)].
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![](docs/pic/Glove_VS_DCWE.png)
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![](../docs/pic/Glove_VS_DCWE.png)
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#### FastText
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The mathematical representation of weight of a term in a document by
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Tf-idf is given:
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![image](docs/eq/tf-idf.gif)
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![image](../docs/eq/tf-idf.gif)
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Where N is number of documents and df(t) is the number of documents
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containing the term t in the corpus. The first part would improve recall
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text classification and/or dimensionality reduction. we start to review
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some random projection techniques.
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![image](docs/pic/Random%20Projection.png)
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![image](../docs/pic/Random%20Projection.png)
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``` {.sourceCode .python}
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from sklearn.feature_extraction.text import TfidfVectorizer
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contains many features, autoencoder could help to process of data faster
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and more efficient.
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![image](docs/pic/Autoencoder.png)
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![image](../docs/pic/Autoencoder.png)
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``` {.sourceCode .python}
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from keras.layers import Input, Dense
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Example of Glove and T-SNE for text:
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![image](docs/pic/TSNE.png)
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![image](../docs/pic/TSNE.png)
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Text Classification Techniques
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------------------------------
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#### Boosting
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![image](docs/pic/Boosting.PNG)
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![image](../docs/pic/Boosting.PNG)
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**Boosting** is a Ensemble learning meta-algorithm for primarily
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reducing Supervised learning, and also variance in supervised learning,
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calculated using an expensive five-fold cross-validation (see Scores
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and probabilities, below).
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![image](docs/pic/SVM.png)
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![image](../docs/pic/SVM.png)
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``` {.sourceCode .python}
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from sklearn.svm import LinearSVC
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Breiman](https://link.springer.com/article/10.1023/A:1010933404324) in
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1999 that they find converge for RF as margin measure.
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![image](docs/pic/RF.png)
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![image](../docs/pic/RF.png)
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``` {.sourceCode .python}
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from sklearn.ensemble import RandomForestClassifier

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