Code Along for BERTweet: A Transformer Model for Sentiment Analysis on Twitter Data

Step 1: Set up the Environment

To set up the environment, we need to install the required libraries and frameworks, including Hugging Face’s Transformers and TensorFlow. We also need to 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

Step 2: Preprocess the Data

In this step, we will download a Twitter sentiment analysis dataset, such as the Sentiment140 dataset, which contains 1.6 million tweets labeled with sentiment. We will load the dataset and extract the tweet text and sentiment labels. Then, we will clean the text data by removing special characters, URLs, and user mentions. Finally, we will 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)

Step 3: Fine-tune BERTweet

In this step, we will fine-tune the BERTweet model for sentiment analysis. We will load the pre-trained BERTweet model from the Hugging Face model repository and tokenize the text data using the BERTweet tokenizer. Then, we will convert the tokenized data into a format suitable for training the sentiment analysis model. Next, we will define the model architecture, including the BERTweet layer and a classification layer. We will implement the fine-tuning process, where the weights of the BERTweet layer are updated based on the sentiment classification task. Finally, we will set up the training parameters and 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 =
val_dataset =

# 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']), validation_data=val_dataset.batch(16), epochs=3)

Step 4: Evaluate the Model

In this step, we will evaluate the performance of the BERTweet model. We will use standard evaluation metrics, such as accuracy, precision, recall, and F1 score. First, we will apply the trained model to the testing data and generate predictions. Then, we will 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)

Step 5: Deploy the Model

In this step, we will save the trained BERTweet model for future use. We will also build a simple user interface that allows users to input their own tweets for sentiment analysis. Finally, we will integrate the deployed model with the user interface to provide real-time sentiment analysis predictions.


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':
    sentiment = predict_sentiment(tweet)
    print('Sentiment:', sentiment)

This is a detailed breakdown of the code snippets for each step. You can follow these steps to implement the BERTweet model for sentiment analysis on Twitter data.