Convolutional-Neural-Network-CNN-for-Text-Classification

Convolutional Neural Network (CNN) for Text Classification in different categories

Dataset:

Categories	No. of Examples
politics	3626
sports	1963
Technology	2134
Food & drink	2543
entertainment	541
Promotion	876
Family	297
travel	297
education	133
style & beauty	74

Dataset is made by combining different opensource datasets.

Requirements:

pip install --user --requirement requirements.txt

  OpenCV==3.4.1
  openpyxl==2.6.2
  xlrd==1.2.0
  XlsxWriter==1.1.8
  xlutils==2.0.0
  xlwings==0.15.8
  xlwt==1.3.0

Steps:

  -Dataset preparation
      >Loading the dataset
      >Dataset Features Counting
  -Feature Engineering
      >Tokenizing
      >Word Embeddings as features
  -Train Test Spliting
  -Text Tokenizing
  -Word Embedding
  -Categorical Veriable Handling
  -CNN Model Building
  -Training the model
  -Testing the model
  -Testing on New Data File

Note:

-Download wordtovector model:

Download the the 'wikinews300d1mvec.zip' and place the wiki-news-300d-1M.vec in the Model directory.

-Download the pretrained our weights and place them in Model directort if you want to test directly. Download Link

CNN Model Architecture

Prediction Result on Test Data: