10-Minute Guide to High-Accuracy Chinese Sentiment Analysis

10-Minute Guide to High-Accuracy Chinese Sentiment Analysis#

1. Install PaddleNLP#

For installation procedures and troubleshooting, please refer to the [Installation Documentation](https://paddlenlp.readthedocs.io/en/latest/gettingstarted/install.html).

>>> pip install --upgrade paddlenlp -i https://pypi.org/simple

2. One-Click Loading of Pretrained Models#

Sentiment analysis is essentially a text classification task. PaddleNLP provides various pretrained models including ERNIE, BERT, RoBERTa, and Electra, along with fine-tuning networks for different downstream tasks. Let’s use ERNIE as an example.

Load ERNIE model:

>>> MODEL_NAME = "ernie-3.0-medium-zh"
>>> ernie_model = paddlenlp.transformers.ErnieModel.from_pretrained(MODEL_NAME)

Load text classification head:

>>> model = paddlenlp.transformers.ErnieForSequenceClassification.from_pretrained(
...     MODEL_NAME, num_classes=len(label_list))

3. Data Processing with Tokenizer#

Load tokenizer:

>>> tokenizer = paddlenlp.transformers.ErnieTokenizer.from_pretrained(MODEL_NAME)

Text processing example:

>>> encoded_text = tokenizer(text="Please input test sample")

Convert to tensor:

>>> input_ids = paddle.to_tensor([encoded_text['input_ids']])
>>> token_type_ids = paddle.to_tensor([encoded_text['token_type_ids']])

Model inference:

>>> sequence_output, pooled_output = ernie_model(input_ids, token_type_ids)
>>> print(f"Token wise output: {sequence_output.shape}, Pooled output: {pooled_output.shape}")

4. Load Dataset#

Load ChnSenticorp dataset:

>>> train_ds, dev_ds, test_ds = paddlenlp.datasets.load_dataset(
...     'chnsenticorp', splits=['train', 'dev', 'test'])

Get label list:

>>> label_list = train_ds.label_list
>>> print(label_list)

Sample data:

>>> for idx in range(5):
...     print(train_ds[idx])

5. Model Training and Evaluation#

(Note: The original content ends here, so the translation stops accordingly while maintaining consistency.) The paddle.io.DataLoader() interface asynchronously loads data with multi-threading, while configuring dynamic learning rates, loss functions, optimization algorithms, and evaluation metrics suitable for Transformer models like ERNIE.

The model training process typically follows these steps:

  1. Fetch a batch of data from the dataloader.

  2. Feed the batch data to the model for forward computation.

  3. Pass the forward computation results to the loss function to calculate loss, and to evaluation metrics to compute performance metrics.

  4. Perform backpropagation with the loss to update gradients. Repeat the above steps.

  5. After each epoch, the program evaluates the model’s current performance.

This example is also available on AIStudio for _online model training experience_.

Finally, save the trained model for prediction.

6. Model Prediction#

After saving the trained model, define the prediction function predict() to perform sentiment analysis.

Example with custom prediction data and labels:

>>> data = [
...     'This hotel is rather outdated, and the discounted rooms are mediocre. Overall average',
...     'Started watching with great excitement, but found a Mickey Mouse cartoon appearing after the main feature',
...     'As an established four-star hotel, the rooms remain well-kept and impressive. The airport shuttle service is excellent, allowing check-in during the ride to save time.',
... ]
>>> label_map = {0: 'negative', 1: 'positive'}

Prediction results:

>>> results = predict(
...     model, data, tokenizer, label_map, batch_size=batch_size)
>>> for idx, text in enumerate(data):
...     print('Data: {} \t Label: {}'.format(text, results[idx]))
Data: This hotel is rather outdated, and the discounted rooms are mediocre. Overall average          Label: negative
Data: Started watching with great excitement, but found a Mickey Mouse cartoon appearing after the main feature      Label: negative
Data: As an established four-star hotel, the rooms remain well-kept and impressive. The airport shuttle service is excellent, allowing check-in during the ride to save time.        Label: positive
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