BERTweet: A Transformer Model for Sentiment Analysis on Twitter Data

ddas · April 3, 2024, 5:01am

Objective: The objective of this project is to leverage the power of BERT (Bidirectional Encoder Representations from Transformers) to develop a transformer-based model, BERTweet, specifically tailored for sentiment analysis on Twitter data. By fine-tuning BERTweet on a large-scale Twitter dataset, we aim to create a highly accurate and efficient model that can effectively understand and classify the sentiment expressed in tweets.

Learning Outcomes: By working on this project, you will:

Gain a deep understanding of BERT and its application in natural language processing tasks.
Learn how to preprocess and transform text data, specifically for Twitter sentiment analysis.
Acquire knowledge and skills in fine-tuning transformer models using machine learning techniques.
Develop expertise in evaluating and interpreting the performance of a sentiment analysis model.
Understand the challenges and considerations involved in applying NLP techniques to social media data.

Steps and Tasks:

Set up the Environment

Install the required libraries and frameworks, including Hugging Face’s Transformers and TensorFlow.
Import the necessary modules and packages for data processing, model training, and evaluation.

Preprocess the Data

Download a Twitter sentiment analysis dataset, such as the Sentiment140 dataset, which contains 1.6 million tweets labeled with sentiment.
Load the dataset and extract the tweet text and sentiment labels.
Clean the text data by removing special characters, URLs, and user mentions.
Split the dataset into training, validation, and testing sets.

Fine-tune BERTweet

Load the pre-trained BERTweet model from the Hugging Face model repository.
Tokenize the text data using the BERTweet tokenizer.
Convert the tokenized data into a format suitable for training the sentiment analysis model.
Define the model architecture, including the BERTweet layer and a classification layer.
Implement the fine-tuning process, where the weights of the BERTweet layer are updated based on the sentiment classification task.
Set up the training parameters, such as the learning rate, batch size, and number of epochs.
Train the BERTweet model using the preprocessed Twitter sentiment analysis dataset.

Evaluate the Model

Use standard evaluation metrics, such as accuracy, precision, recall, and F1 score, to assess the performance of the BERTweet model.
Apply the trained model to the testing data and generate predictions.
Calculate the evaluation metrics based on the predicted sentiment labels and compare them to the performance of other sentiment analysis models.

Deploy the Model

Save the trained BERTweet model for future use.
Build a simple user interface that allows users to input their own tweets for sentiment analysis.
Integrate the deployed model with the user interface to provide real-time sentiment analysis predictions.

Evaluation:

The project will be evaluated based on the performance of the developed BERTweet model in sentiment analysis tasks.
Additionally, the clarity and thoroughness of the code, data preprocessing steps, and model evaluation will be assessed.

Resources and Learning Materials:

BERT: https://arxiv.org/abs/1810.04805
Hugging Face Transformers: https://huggingface.co/transformers/
Sentiment140 Dataset: https://www.kaggle.com/kazanova/sentiment140
Twitter Sentiment Analysis with BERT: https://www.analyticsvidhya.com/blog/2020/07/twitter-sentiment-analysis-using-transformerxl-aka-bert-with-hugging-face/
How to Fine-Tune BERT for Text Classification: https://arxiv.org/abs/1905.05583
Building a Sentiment Analysis Python Model: https://towardsdatascience.com/building-a-sentiment-analysis-python-model-6edc1e8df653

Need a little extra help? Here’s a detailed breakdown of the code snippets for each step:

1. Set up the Environment

Install the required libraries and frameworks, including Hugging Face’s Transformers and TensorFlow.
Import the necessary modules and packages for data processing, model training, and evaluation.

!pip install transformers tensorflow

import tensorflow as tf
from transformers import TFBertModel, BertTokenizer

2. Preprocess the Data

Download a Twitter sentiment analysis dataset, such as the Sentiment140 dataset, which contains 1.6 million tweets labeled with sentiment.
Load the dataset and extract the tweet text and sentiment labels.
Clean the text data by removing special characters, URLs, and user mentions.
Split the dataset into training, validation, and testing sets.

import pandas as pd
import re
from sklearn.model_selection import train_test_split

# Load the dataset
data = pd.read_csv('sentiment140.csv', encoding='latin-1', header=None)

# Extract tweet text and sentiment labels
tweets = data[5].values
labels = data[0].values

# Clean the text data
def clean_text(text):
    text = re.sub(r'@[A-Za-z0-9_]+', '', text)  # Remove user mentions
    text = re.sub(r'https?://[A-Za-z0-9_./]+', '', text)  # Remove URLs
    text = re.sub(r'[^A-Za-z0-9 ]+', '', text)  # Remove special characters
    text = text.lower()  # Convert to lowercase
    return text

cleaned_tweets = [clean_text(tweet) for tweet in tweets]

# Split the dataset into training, validation, and testing sets
train_tweets, test_tweets, train_labels, test_labels = train_test_split(cleaned_tweets, labels, test_size=0.2, random_state=42)
train_tweets, val_tweets, train_labels, val_labels = train_test_split(train_tweets, train_labels, test_size=0.2, random_state=42)

3. Fine-tune BERTweet

Load the pre-trained BERTweet model from the Hugging Face model repository.
Tokenize the text data using the BERTweet tokenizer.
Convert the tokenized data into a format suitable for training the sentiment analysis model.
Define the model architecture, including the BERTweet layer and a classification layer.
Implement the fine-tuning process, where the weights of the BERTweet layer are updated based on the sentiment classification task.
Set up the training parameters, such as the learning rate, batch size, and number of epochs.
Train the BERTweet model using the preprocessed Twitter sentiment analysis dataset.

# Load the BERTweet model and tokenizer
model = TFBertModel.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Tokenize the text data
train_encodings = tokenizer(train_tweets, truncation=True, padding=True)
val_encodings = tokenizer(val_tweets, truncation=True, padding=True)
test_encodings = tokenizer(test_tweets, truncation=True, padding=True)

# Convert the tokenized data into a TensorFlow Dataset
train_dataset = tf.data.Dataset.from_tensor_slices((
    dict(train_encodings),
    train_labels
))
val_dataset = tf.data.Dataset.from_tensor_slices((
    dict(val_encodings),
    val_labels
))

# Define the model architecture
input_ids = tf.keras.Input(shape=(None,), dtype='int32')
attention_mask = tf.keras.Input(shape=(None,), dtype='int32')
embeddings = model(input_ids, attention_mask=attention_mask)[0]
output = embeddings[:, 0, :]  # Use the [CLS] token for classification
output = tf.keras.layers.Dense(1, activation='sigmoid')(output)
model = tf.keras.Model(inputs=[input_ids, attention_mask], outputs=output)

# Fine-tune the BERTweet model
optimizer = tf.keras.optimizers.Adam(learning_rate=1e-5)
model.compile(optimizer=optimizer, loss=tf.keras.losses.BinaryCrossentropy(), metrics=['accuracy'])
model.fit(train_dataset.shuffle(1000).batch(16), validation_data=val_dataset.batch(16), epochs=3)

4. Evaluate the Model

Use standard evaluation metrics, such as accuracy, precision, recall, and F1 score, to assess the performance of the BERTweet model.
Apply the trained model to the testing data and generate predictions.
Calculate the evaluation metrics based on the predicted sentiment labels and compare them to the performance of other sentiment analysis models.

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# Evaluate the model
test_loss, test_accuracy = model.evaluate(test_dataset.batch(16))
print('Test Loss:', test_loss)
print('Test Accuracy:', test_accuracy)

# Generate predictions
y_pred = model.predict(test_dataset.batch(16))
y_pred = y_pred.squeeze() > 0.5

# Calculate evaluation metrics
accuracy = accuracy_score(test_labels, y_pred)
precision = precision_score(test_labels, y_pred)
recall = recall_score(test_labels, y_pred)
f1 = f1_score(test_labels, y_pred)

print('Accuracy:', accuracy)
print('Precision:', precision)
print('Recall:', recall)
print('F1 Score:', f1)

5. Deploy the Model

Save the trained BERTweet model for future use.
Build a simple user interface that allows users to input their own tweets for sentiment analysis.
Integrate the deployed model with the user interface to provide real-time sentiment analysis predictions.

model.save_pretrained('bertweet_sentiment_analysis')

loaded_model = TFBertModel.from_pretrained('bertweet_sentiment_analysis')
loaded_tokenizer = BertTokenizer.from_pretrained('bertweet_sentiment_analysis')

def predict_sentiment(tweet):
    encoding = loaded_tokenizer.encode_plus(tweet, return_tensors='tf', padding=True, truncation=True)
    input_ids = encoding['input_ids']
    attention_mask = encoding['attention_mask']
    prediction = loaded_model.predict([input_ids, attention_mask])[0]
    sentiment = 'positive' if prediction > 0.5 else 'negative'
    return sentiment

# User interface
while True:
    tweet = input('Enter a tweet (or "quit" to exit): ')
    if tweet == 'quit':
        break
    sentiment = predict_sentiment(tweet)
    print('Sentiment:', sentiment)

stemaway · April 3, 2024, 5:01am

@joy.b has been assigned as the mentor. View code along.