Sentiment Analysis Ktrain

Sentiment Analysis with ktrain and BERT

This tutorial demonstrates how to perform sentiment analysis using the ktrain library and the Amazon Digital Music Reviews dataset. We will leverage BERT as our classification model through ktrain's user-friendly, low-code interface.

Prerequisites

• Python installed

• pip package installer

Setup

Step 1: Install ktrain

First, install the ktrain library using pip:

!pip install ktrain

Step 2: Import Required Libraries

Import the necessary libraries for our sentiment analysis task:

import ktrain
from ktrain import text
import pandas as pd

Data Preparation

Step 3: Load the Dataset

We'll use the Amazon Digital Music Reviews dataset. You can download it from its official source or use the provided Google Drive link for convenience.

## Download the dataset using gdown (install gdown if you don't have it: !pip install gdown)
!gdown https://drive.google.com/uc?id=1-8urBLVtFuuvAVHi0s000e7r0KPUgt9fdf

## Load the dataset into a pandas DataFrame
df = pd.read_json('reviews_Digital_Music_5.json', lines=True)
df.head()

Step 4: Prepare the Data

We need to select relevant columns and map star ratings to sentiment labels.

1. Select relevant columns: Keep only reviewText and overall.

2. Map star ratings to sentiment:

   • 1, 2, 3 stars are mapped to 'negative'.

   • 4, 5 stars are mapped to 'positive'.

## Keep only the review text and overall rating
df = df[['reviewText', 'overall']]

## Define a sentiment mapping
sentiment_map = {1: 'negative', 2: 'negative', 3: 'negative', 4: 'positive', 5: 'positive'}

## Apply the mapping to create the sentiment column
df['sentiment'] = df['overall'].map(sentiment_map)

## Keep only the text and sentiment columns
df = df[['reviewText', 'sentiment']]
df.head()

Model Building and Training

Step 5: Split Data for Training and Testing

ktrain provides texts_from_df to preprocess text data and split it into training and testing sets. We'll specify BERT preprocessing.

• text_column: The column containing the review text.

• label_columns: The column containing the sentiment labels.

• maxlen: Maximum sequence length for BERT (typically 128 or 256, 100 is used here for demonstration).

• max_features: Maximum number of unique words to consider (relevant for non-BERT models, but included for completeness).

• preprocess_mode: Set to 'bert' to enable BERT-specific preprocessing.

• val_pct: Percentage of data to reserve for validation.

(x_train, y_train), (x_test, y_test), preproc = text.texts_from_df(
    train_df=df,
    text_column='reviewText',
    label_columns=['sentiment'],
    maxlen=100,
    max_features=100000, # Not critical for BERT, but good practice
    preprocess_mode='bert',
    val_pct=0.1  # 10% for validation
)

Step 6: View Available Classifiers in ktrain

ktrain supports various text classification models. You can list them to see your options:

text.print_text_classifiers()

Commonly available classifiers include: logreg, nbsvm, bigru, bert, and distilbert. For this tutorial, we will use BERT.

Step 7: Build the BERT Classifier

Now, let's build the BERT classification model using ktrain.

• name: Specify the classifier name ('bert').

• train_data: The training data tuple (x_train, y_train).

• preproc: The preprocessing object obtained in the previous step.

• metrics: Metrics to monitor during training (e.g., 'accuracy').

model = text.text_classifier(
    name='bert',
    train_data=(x_train, y_train),
    preproc=preproc,
    metrics=['accuracy']
)

Step 8: Create the Learner Instance

The ktrain.get_learner function creates a Learner object, which is used to manage the training process.

• model: The built Keras model.

• train_data: The training data.

• val_data: The validation data.

• batch_size: The number of samples per gradient update.

• use_multiprocessing: Enables multiprocessing for faster data loading.

learner = ktrain.get_learner(
    model=model,
    train_data=(x_train, y_train),
    val_data=(x_test, y_test),
    batch_size=32,
    use_multiprocessing=True
)

Step 9: Train the Model

We will train the model using the fit_onecycle method, which implements a learning rate schedule known for effective training.

• lr: The learning rate.

• epochs: The number of training epochs.

• checkpoint_folder: Directory to save model checkpoints.

learner.fit_onecycle(
    lr=2e-5,      # Learning rate
    epochs=1,     # Train for 1 epoch for demonstration
    checkpoint_folder='output' # Folder to save checkpoints
)

Example Output:

begin training using onecycle policy with max lr of 2e-05...
loss: 0.3573 - accuracy: 0.8482 - val_loss: 0.2991 - val_accuracy: 0.8778

Observation: The model achieves approximately 88% validation accuracy after just one epoch, demonstrating the power of BERT and ktrain.

Prediction and Saving

Step 10: Save the Predictor and Make Predictions

After training, you can create a Predictor object to easily make predictions on new text.

predictor = ktrain.get_predictor(learner.model, preproc)

## Make a prediction on a new review
prediction = predictor.predict('I loved the song')
print(f"Prediction for 'I loved the song': {prediction}")

prediction_negative = predictor.predict('This was a terrible experience.')
print(f"Prediction for 'This was a terrible experience.': {prediction_negative}")

Example Output:

Prediction for 'I loved the song': positive
Prediction for 'This was a terrible experience.': negative

Conclusion

ktrain significantly simplifies the process of building and training powerful Natural Language Processing (NLP) models, such as sentiment analysis with BERT. Its low-code interface allows you to achieve strong performance with minimal code, making advanced deep learning techniques accessible.

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Interview Questions

Here are some common interview questions related to this topic:

1. What is ktrain and how does it simplify machine learning model development? ktrain is a Python library that provides a high-level API for deep learning and traditional machine learning, making it easier and faster to develop and deploy models. It abstracts away much of the boilerplate code often associated with libraries like TensorFlow and Keras.

2. How does ktrain integrate with TensorFlow and Keras? ktrain is built on top of TensorFlow and Keras, leveraging their capabilities for building, training, and deploying neural networks. It acts as a wrapper, providing a more intuitive interface to these underlying frameworks.

3. What are the main advantages of using ktrain for NLP tasks?

   • Low-code API: Significantly reduces the amount of code needed.

   • Pre-trained Models: Easy access to and fine-tuning of state-of-the-art models like BERT, DistilBERT, etc.

   • Integrated Workflow: Handles data preprocessing, model building, training, evaluation, and deployment seamlessly.

   • Efficiency: Optimized for speed and performance.

   • Versatility: Supports various NLP tasks beyond sentiment analysis, like text classification, entity recognition, summarization, and more.

4. How do you prepare text data for classification using ktrain? ktrain.text.texts_from_df or ktrain.text.texts_from_array are primary functions. They handle tokenization, padding/truncation, vocabulary creation, and converting text to numerical representations suitable for neural networks, often with specific modes like 'bert' for BERT-based models.

5. What is the role of the texts_from_df function in ktrain? This function is crucial for loading and preprocessing tabular text data. It takes a Pandas DataFrame, identifies the text and label columns, applies specified preprocessing (like BERT tokenization), and splits the data into training, validation, and test sets. It also returns a Preprocessor object essential for later prediction.

6. How does ktrain help in fine-tuning pre-trained models like BERT? ktrain provides the text.text_classifier function where you can specify name='bert' (or other pre-trained models). It automatically downloads the pre-trained weights, configures the model architecture for the specified task, and then you can fine-tune it on your custom dataset using methods like fit_onecycle.

7. What are some common metrics used to evaluate a sentiment classification model?

   • Accuracy: The proportion of correctly classified instances.

   • Precision: The proportion of true positives among all predicted positives.

   • Recall: The proportion of true positives among all actual positives.

   • F1-Score: The harmonic mean of precision and recall.

   • Confusion Matrix: A table showing true positive, true negative, false positive, and false negative counts.

   • ROC AUC: For binary classification, it measures the ability of the model to distinguish between classes.

8. How would you save and reuse a trained model with ktrain? After training, you create a Predictor object using ktrain.get_predictor(learner.model, preproc). You can then save this predictor using predictor.save('path/to/predictor'). To reuse it, load it with ktrain.load_predictor('path/to/predictor') and then use its predict() method.

9. Explain the concept of “one-cycle policy” used during training. The one-cycle policy is a learning rate scheduling technique proposed by Leslie N. Smith. It involves a single cycle where the learning rate increases from a low value to a maximum value and then decreases back down. This can often lead to faster convergence and better generalization compared to fixed learning rates or simple decay schedules. ktrain.fit_onecycle implements this.

10. Can ktrain be used for tasks other than sentiment analysis? If yes, give examples. Yes, absolutely. ktrain is versatile and supports a wide range of NLP tasks:

    • Text Classification: Spam detection, topic categorization, intent recognition.

    • Named Entity Recognition (NER): Identifying and classifying entities (people, organizations, locations) in text.

    • Text Summarization: Generating concise summaries of longer texts.

    • Question Answering: Finding answers to questions within a given text.

    • Zero-Shot Classification: Classifying text into categories without specific training examples for those categories.

    • Image Classification: ktrain also has strong capabilities for computer vision tasks.