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Text Classification using CNN

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Yutong Wang edited this page Sep 5, 2019 · 17 revisions

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🚘 How to use CNN to do classification on tweets text?

Contents

  • Data Labeling Standard
  • Dataset Preparation
  • TextCNN Module Architecture
  • Word Embedding
  • Training & Testing
  • Performance
  • Author

Data Labeling Standard [TO BE IMPROVED]

For each tweets record, it needs three labeler: Label1, Label2, and Judge. The label should be 0, 1 or 2.
0 represents for true, 1 represents for false, and 2 represents for not sure.

  • When should a labeler label a record to be true?
    If a tweet is talking about a real wildfire or some wildfire smoke in recent time(not history), and within a close distance, then we label this record to be true. Here follows some examples:

    • "Wildfire just to the north of us Windy as hell #sonomacounty #kron4 #wildfire"
    • "Biggest threat to homes this am is wildfire coming down from Annadel State Park to White Oak. Calm winds mean fire slow moving. @CBSSF"
    • "Oregons Wildfire right now is hella fucking up washingtons Sky like I can almost stare at the sun and not be affected."

  • When should a labeler label a record to be false?
    If a tweet is not talking about a real wildfire or smoke, or is talking about historical wildfires or wildfires very far away, then we label it to be false. Here follows some examples:

    • "Wow, Wildfire is such a good song @ddlovato"
    • "BREAKING: Illegal Muslim From Iran Arrested For Starting California Wildfire"

  • When should a labeler label a record to be not sure?
    If a tweet seems to talking about a real wildfire but the expression is equivocal. You can't tell the exact information like when and where the wildfire happens. Here follows some examples:

    • "Wildfire...."
    • "It's so hazy out here in Hood River. I wonder if it's smoke from the wildfires? I thought they were pretty far away... A new wildfire?"

Dataset Preparation

For more details of building dataset and dataloader, please checkout to yutong-text-cnn branch and check Wildfires/yutong_nlp/textcnn_mine/mydatasets_embed.py.

training dataset

Use this sql to select 468 tweets records from database which are labeled to be true(0).

SELECT text from records where label1 = 0

Use this sql to select 532 tweets records from database which are labeled to be false(1).

SELECT text from records where label1 = 1

So in all the training dataset contains 1000 different tweet records.

testing dataset

Use similar sql statement to select 528 tweets records from database where label2 = 0 and label1 is null or label2 = 1 and label1 is null, in order to make testing data different from training data.

TextCNN Module Architecture

The architecture of CNN used to classifying tweets text follows the work in Convolutional neural networks for sentence classification.

The following code defines the architecture of CNN.

  • The parameter vocab_len is the length of vocabulary for word embedding and weights is the initial weights for word embedding.

  • The first layer of the network is the Embedding layer. User can decide to use single or multiple embedding layers. After embedding, each word, which is represented by index, will be transformed to a vector with length 300.

  • Then define three parallel convolutional layers, following three ReLU layers and three MaxPool layers.

  • After the final softmax layer, you can get a vector of length 2 for each tweet, indicating the probability result of the tweet to be wildfire or not wildfire related. To be detail, the probability in position of index 0 represents the probability to be real wildfire.

class CNN_Text(nn.Module):

    def __init__(self, args, vocab_len, weights):
        super(CNN_Text, self).__init__()
        self.args = args

        # parameters
        V = vocab_len
        D = args.embed_dim
        C = 2
        Ci = 1  # conv layer input dimention
        Co = args.kernel_num  # conv layer output dimention
        Ks_str = args.kernel_sizes
        Ks = [int(k) for k in Ks_str.split(',')]

        # embedding layer, input_size = V, output_size = D
        self.embed = nn.Embedding(V, D)

        if args.glove_embed:
            self.embed.weight = nn.Parameter(torch.FloatTensor(weights), requires_grad=args.glove_embed_train)
        if args.multichannel:
            self.embed1 = nn.Embedding(V, D)
            self.embed1.weight = nn.Parameter(torch.FloatTensor(weights), requires_grad=False)
            Ci = 2

        # self.convs1 = nn.ModuleList([nn.Conv2d(Ci, Co, (K, D)) for K in Ks])
        self.convs1 = nn.ModuleList([nn.Conv2d(Ci, Co, (1, 300)) for K in Ks])
        self.dropout = nn.Dropout(args.dropout)
        self.fc1 = nn.Linear(len(Ks) * Co, C)

    def forward(self, input):
        x = self.embed(input)
        x = x.unsqueeze(1)

        if self.args.multichannel:
            x1 = self.embed1(input)
            x1 = x1.unsqueeze(1)
            x = torch.cat((x, x1), 1)

        x = [F.relu(conv(x)).squeeze(3) for conv in self.convs1]

        x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in x]

        x = torch.cat(x, 1)

        x = self.dropout(x)

        logit = F.softmax(self.fc1(x), dim=-1)

        return logit

Word Embedding

To embed each word as a vector, I download an existed embedding file GoogleNews-vectors-negative300.bin from website, the vocabulary of which contains 3,000,000 different words. Through embedding, each word's dimension changes from [1, 1] to [1, 300].

gensim_model = KeyedVectors.load_word2vec_format(self.read_path + 'GoogleNews-vectors-negative300.bin',
                                                         binary=True)
vocab = gensim_model.vocab
vocab_len = len(vocab)
weights = gensim_model.vectors

Training & Testing

For more details of training and testing process, please checkout to yutong-text-cnn branch and check Wildfires/yutong_nlp/textcnn_mine/train.py. And for more details of all the arguments value, please checkout to yutong-text-cnn branch and check Wildfires/yutong_nlp/textcnn_mine/main.py.

Here are several highlighted training parameters:

  • learning rate: 0.001
  • epochs: 4
  • batch size: 64
  • padding length: 64
  • optimizer: Adam
  • loss function: Cross Entropy

To train the model, use following code to call the train method:

import train
train.train(train_loader, validate_loader, test_loader, model, my_loss, weight, args)

To test the model during training process, call train.eval method to evaluate the accuracy and loss of the model.

Performance

Model Name Accuracy Specificity Precision Recall (TPR) F1 False Positive Rate (FPR)
TextCNN 0.8201 0.8658 0.8826 0.7845 0.8307 0.1342

Author

Yutong Wang / @RainyTong

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