**Emotion Detection using Transfer Learning**

Emotion Detection using Transfer Learning

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)

This code snippet utilizes transfer learning to leverage a pre-trained sentiment analysis model for emotion detection. It loads a pre-trained model, pads input sequences to ensure equal lengths, and then uses the model to predict emotions. This approach is particularly useful when working with limited labeled data and aims to reduce the computational overhead associated with training a model from scratch. By leveraging the knowledge embedded in the pre-trained model, this code efficiently detects emotions in text data with high accuracy.

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