**Emotion Detection using Transfer Learning**

Source: Dev.to

Overview

This snippet demonstrates how to use transfer learning for emotion detection by leveraging a pre‑trained sentiment analysis model. The model is loaded, input text sequences are padded to a uniform length, and the model predicts emotion probabilities for each sequence. This approach is especially valuable when labeled data is scarce, as it avoids the need to train a model from scratch while still delivering accurate emotion classification.

Code Example

from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import load_model

# Load a pre-trained sentiment analysis model
model = load_model('sentiment_analysis_model.h5')

# Pad sequences to ensure equal length
padded_sequences = pad_sequences(text_data, maxlen=200)

# Perform emotion detection
emotions = model.predict(padded_sequences)
print(emotions)