Create customer sentiment

customer sentiment

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+from transformers import pipeline
+
+# Initialize the Hugging Face zero-shot classification pipeline
+classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+
+# Review text to analyze
+review_text = "The product's quality was poor, and customer service was useless. I was quite unsatisfied with my experience."
+
+# Define the labels for sentiment classification
+labels = ["POSITIVE", "NEUTRAL", "NEGATIVE"]
+
+# Perform zero-shot classification
+result = classifier(review_text, labels)
+
+# Print the results
+print("Review Analysis:")
+for i, label in enumerate(result['labels']):
+    print(f"{label}: {result['scores'][i]:.4f}")
+
+# Output the label with the highest score as the predicted sentiment
+predicted_sentiment = result['labels'][0]  # The label with the highest score is at index 0
+print(f"\nPredicted Sentiment: {predicted_sentiment}")