Deep Text Classifier

Deep Text Classifier

Problem Description

We want to build a classifier for the text input. For example, we may want to classify a company's industry area based on its description. Or we may want to classify a company's IPO status based on its description.

General Approach

We will build a deep neural network to uniformly solve this problem. The traditional way of doing this is to hire a task specific expert to manually design some useful features, say to check if the text contains words "Internet" and "High-tech" at the same time, and to classify based on the observed features. Our way, by using the deep neural network, can automatically extract the features and most importantly achieve very high testing accuracy. However, the features that are used by the deep neural network are not human interpretable.

About the Deep Models

There are basically two big categories of deep neural networks - the convolutional neural networks CNN and the recurrent neural networks RNN. The first one, CNN, is more suitable for dealing with the image based classification tasks. The second one, RNN, is in general for sequential information (i.e. language, video ...) based classification tasks. I have tired both kinds of models and , as expected, the RNN is more robust in facing different text classification tasks

Major Package Dependences

• Tensorflow https://www.tensorflow.org/
• Numpy http://www.numpy.org/
• Keras https://keras.io/

How to Run the Code

The code contains two parts: Data Preprocessing and Model Training/Prediction.

Data Preprocessing (preprocessing.py) : this is where you transfer a text based "XXX.txt" input file into a numerical value based pickle file that the later part of the code can understand and use for training and prediction.

• Step 1 : specify the target file name in "main()"

  # don't add ".txt" extension
  file_name = 'ThicketDefCodingTestProcessed'

• Step 2 : specify the expected columns of your target file in "main()"

  # expected number of columns, in case we have "None" in the table
  expected_columns = 5

• Step 3 : specify the indices of the text and the label in "prepare_imdb_structure(file_name, expected_columns)"

  # the index of the label in the tokens
  label_index = 1
  # the index of the text in the tokens
  content_index = 4

• Step 4 : run the code

  python preprocessing.py 

• Step 5 : give your pickle file a more reasonable name

Attention: by default, the name of the pickle file is same as the original ".txt" file. But it's highly likely that you will use the same text inputs to predict different things. So it's important to give your pickle file a more reasonable name each time you run the above script. For example, from "longdescriptions.pkl" to "longdescriptions_indu.pkl" to indicate that we are predicting the industry areas and to "longdescriptions_ipo.pkl" to indicate that we are predicting the IPO status. If you don't do this, the later generated pickle files will overwrite the previously generated ones. 

Model Training/Prediction (classification_MMM_LLL.py) : this is where the deep neural network is. The "MMM" represents the model. For example, currently I have "1DConvolution", "2DConvolution" and "LSTM". "LLL" represents the name of the label. Notice that for the same text inputs we can predict for different things using the same model literally. For example, "classification_LSTM_indu.py" is a LSTM model to predict the industray based on the descriptions. And "classification_LSTM_ipo.py" is a LSTM model to predict the IPO status based on the same descriptions. Again you need to name your files properly! Different tasks will have different hyper-parameter configurations though the model and the inputs can be totally the same. This Python file, no matter what the model is, will always load in a pickle file you generated in the previous step and train the neural network. At the end, the well trained neural network will predict on your test examples (the examples you don't see during the training) and print the accuracy.

• Step 1 : specify the name of the pickle file

with open('longdescription_ipo.pkl', 'rb') as file:

• Step 2 : specify the total number of possible labels

model.add(Dense(2, activation='softmax'))

• Step 3 : run the code

python classification_LSTM_ipo.py

Data Preprocessing

For data preprocessing, we adopt the same standard as in the IMDB dataset.

To general users:

Your input file (usually a single ".txt" file contains many examples each as a row) will be split into a training set (80% by default) and a testing set (20% by default). The labels you want to predict will be the folder names. The content (usually a block of text) of the examples will go into separate ".txt" files. To run the script, you basically need to specify the following:

 1. "File Name" : without the ".txt" extension,
 2. "Expected Columns" : total number of columns in the input file
 3. "Content Index" : the column index of the content 
 4. "Label Index" : the column index of the label

The script will generate a pickle file with an ".pkl" extension and the name will be the same as your input. Please change the name properly to indicate the label information as have been discussed above. And place this pickle file under the same directory with your classification code, i.e. "classification_MMM_LLL.py"

To advanced users:

1. One important step in data preprocessing is to encode words (strings) to integers. That is we need to build a dictionary mapping words to their corresponding indices. Our dictionary is ordered by the words' frequency. Higher the frequency smaller the index, i.e. you should expect to see "the, a, ..." these words in the smallest 10 indices : 2, 3, 4, .... Please also notice that 0 and 1 these two indices are not assigned to any words intentionally. The advantage of doing this is that you can specify easily ignore those common and meaningless words by simply say I want to consider words with the index > 20 for example. And for any word that is not in our dictionary, code it with index 1, so again you can easily ignore it.

2. Saving a pickle file is an very efficient way to retrieve the data so that you don't need to do preprocessing every time you want to run your classifier.