#### Project Outline
1. **Setup and Libraries**
- **Install Required Libraries:**
```bash
pip install nltk textblob
```
2. **Data Collection**
- **Simulate Feedback Data:** Create a list of sample customer feedback.
```python
feedback = [
"I love your service!",
"The product quality is terrible.",
"Customer support was okay, but could be better.",
"Fantastic experience, will definitely recommend!"
]
```
3. **Sentiment Analysis**
- **Use TextBlob for Sentiment Analysis:**
```python
from textblob import TextBlob
def analyze_sentiment(text):
analysis = TextBlob(text)
return analysis.sentiment.polarity # Returns sentiment polarity (-1 to 1)
# Analyze feedback
sentiments = [analyze_sentiment(fb) for fb in feedback]
```
4. **Generate Responses**
- **Create Response Function Based on Sentiment:**
```python
def generate_response(sentiment_score):
if sentiment_score > 0.5:
return "Thank you for your positive feedback! We're thrilled to hear you enjoyed our service."
elif sentiment_score > 0:
return "Thank you for your feedback! We're glad you had a good experience but will strive to improve further."
elif sentiment_score == 0:
return "Thank you for your feedback. We will review it to understand how we can serve you better."
else:
return "We're sorry to hear about your experience. Please contact support for assistance."
# Generate responses for feedback
responses = [generate_response(score) for score in sentiments]
```
5. **Display Results**
- **Print Feedback with Corresponding Responses:**
```python
for fb, response in zip(feedback, responses):
print(f"Feedback: {fb}")
print(f"Response: {response}")
print()
```
#### Summary
This project analyzes customer feedback to determine its sentiment and generates an appropriate response based on the sentiment score. It involves:
1. **Setting up the environment** and installing necessary libraries.
2. **Simulating data collection** with a list of feedback.
3. **Performing sentiment analysis** using the TextBlob library.
4. **Generating responses** based on sentiment scores.
5. **Displaying results** to show the feedback and corresponding responses.
This project demonstrates natural language processing and basic decision-making in Python.
