6. Use a pre-trained Hugging Face model to analyze sentiment in text. Assume a real-world application, Load the sentiment analysis pipeline. Analyze the sentiment by giving sentences to input.
PROGRAM:
# Step 1: Install required libraries (only run once)
# pip install transformers torch
# Step 2: Import necessary library
from transformers import pipeline
# Step 3: Load the sentiment analysis pipeline using a pre-trained model
sentiment_pipeline = pipeline("sentiment-analysis")
# Step 4: Define input sentences (simulating real-world user reviews)
input_sentences = [
"The new phone I bought is absolutely amazing!",
"Worst customer service ever. I'm never coming back.",
"The experience was average, nothing special.",
"Fast delivery and the packaging was perfect.",
"The product broke within two days. Very disappointed."
]
# Step 5: Perform sentiment analysis
results = sentiment_pipeline(input_sentences)
# Step 6: Display the results
print("Sentiment Analysis Results:\n")
for sentence, result in zip(input_sentences, results):
print(f"Input Sentence: {sentence}")
print(f"Predicted Sentiment: {result['label']}, Confidence Score: {result['score']:.2f}\n")OUTPUT:
Sentiment Analysis Results:
Input Sentence: The new phone I bought is absolutely amazing!
Predicted Sentiment: POSITIVE, Confidence Score: 1.00
Input Sentence: Worst customer service ever. I'm never coming back.
Predicted Sentiment: NEGATIVE, Confidence Score: 1.00
Input Sentence: The experience was average, nothing special.
Predicted Sentiment: NEGATIVE, Confidence Score: 1.00
Input Sentence: Fast delivery and the packaging was perfect.
Predicted Sentiment: POSITIVE, Confidence Score: 1.00
Input Sentence: The product broke within two days. Very disappointed.
Predicted Sentiment: NEGATIVE, Confidence Score: 1.00