Text-Classification

Text Classification with various Machine Learning and Deep Learning architectures:
Written in Keras 2.2.4
ML:

1. Logistic Regression
2. SVM
3. Naive Bayes Classifier
4. Random Forest Classifier
5. Xtreme Gradient Boost Classifier

DL:

1. FCNN
2. CNN
3. BILSTM

Feature Extraction:

1. Count Vectorizer
2. TF-IDF Vectorizer
3. TF-IGM Vectorizer based on this paper https://doi.org/10.1016/j.eswa.2016.09.009 by Chen, Kewen, et.al.,

Setup

The code is written in Python 3.6 To install the required packages:

pip install -r requirements.txt

Use config.json to set the configurations:

{
    "maxlen" : [maximum sentence length],
    "model_type" : ["ML" or "FCNN" or "BILSTM" or "CNN"],
    "batch_size" : [batch size],
    "epochs" : [number of epochs],
    "vectorizer" : ["count" or "tfidf" or "tf-igm"],
    "stopwords_file" : [txt file that contains stopwords],
    "train_file" : [csv file that contains training data with 2 columns "text" and "annotations"],
    "test_file" : [csv file that contains training data with 2 columns "text" and "annotations"]
}

How to run the code:

python main.py