TextSentimentClassification
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TextSentimentClassification, using tensorflow.
TextSentimentClassification
TextSentimentClassification, using tensorflow. Original Data
Data Preprocessing
Remove the letter whose number of repetitions is over 3 from a word...
Word Vectors Training
Using word2vec and GloVe to generate word vectors...
Models
Performance
Model | Epoch | Training Accuracy | Validation Accuracy | Parameters(word vectors excluded) |
---|---|---|---|---|
TextCNN+nonstatic | 130 | 0.8839 | 0.8142 | 281,202 |
TextRNN+nonstatic | 150 | 0.8383 | 0.8199 | 285,826 |
CRNN+nonstatic | 70 | 0.8600 | 0.8219 | 274,818 |
RCNN+nonstatic | 50 | 0.8553 | 0.8227 | 318,978 |
HAN+nonstatic | 110 | 0.8355 | 0.8188 | 209,410 |
TextCNN
Reference
Convolutional Neural Networks for Sentence Classification
Model Architecture
Total 4 ways:
- CNN-rand
- CNN-static
- CNN-nonstatic
- CNN-multichannel
Performance
Model | Epoch | Training Accuracy | Validation Accuracy | Parameters(word vectors excluded) |
---|---|---|---|---|
TextCNN+rand | 130 | 0.8761 | 0.8137 | 281,202 |
TextCNN+static | 60 | 0.9015 | 0.8113 | 281,202 |
TextCNN+nonstatic | 130 | 0.8839 | 0.8142 | 281,202 |
TextCNN+multichannel | 60 | 0.9225 | 0.8141 | 561,202 |
Choosing to use word vectors in a nonstatic way.
TextRNN
Model Architecture
Using bidirectional RNN, and then concatenating the output of the forward process and the output of the backward process...
CRNN
Reference
A C-LSTM Neural Network for Text Classification
Model Architecture
Using CNN to extract sentences with higher-level phrase representations, and then learning long short-term dependency with bi-RNN...
RCNN
Reference
Recurrent Convolutional Neural Networks for Text Classification
Model Architecture
In addition to implementing the same structure as the paper, using bi-LSTM or bi-GRU and then concatenating their outputs... RNN for capturing contextual information and max pooling used for judging which words play key roles in the task...
HAN
Reference
Hierarchical Attention Networks for Document Classification
Model Architecture
Transforming a sentence into a document consisting of sentences...
Ensembles
Bagging
Uniform blending...
Stacking
Using Logistic Regression as the level-2 classifier...
Performance
Model | Epoch | Training Accuracy | Testing Accuracy | Parameters(word vectors excluded) |
---|---|---|---|---|
LR+static_avg | - | 0.77364 | 0.773605 | - |
NB+static_avg | - | 0.606435 | 0.61082 | - |
TextCNN+nonstatic | 130 | 0.8703 | 0.817615 | 281,202 |
TextRNN+nonstatic | 150 | 0.8384 | 0.81969 | 285,826 |
CRNN+nonstatic | 70 | 0.8589 | 0.82449 | 274,818 |
RCNN+nonstatic | 50 | 0.8497 | 0.822935 | 318,978 |
HAN+nonstatic | 110 | 0.8330 | 0.820235 | 209,410 |
bagging | - | 0.8538 | 0.82999 | - |
stacking | - | 0.867135 | 0.831045 | - |