Steps to Build Sentiment Analysis Model (using TextBlob or NLTK)

• By mohsinkhokhar715
• September 11, 2025September 11, 2025
• Education

1. Setup Environment

• Install required libraries: textblob, nltk.
• Download NLTK data (if using NLTK, e.g., stopwords, tokenizer).

2. Prepare Sample Data (Customer Reviews)

• Collect or create a small dataset of text reviews (e.g., “The product is amazing!”, “Very bad service.”).
• Store them in a list or read from a file (CSV/JSON).

3. Preprocess Text (Optional)

• Lowercase the text.
• Remove special characters, numbers, stopwords.
• Tokenize text if needed (for NLTK).

4. Sentiment Analysis with TextBlob

• Use TextBlob(review).sentiment.polarity to get polarity score (range: -1 to 1).
  • > 0 → Positive
  • < 0 → Negative
  • = 0 → Neutral
• Also extract subjectivity for confidence measurement.

5. Sentiment Analysis with NLTK (Optional)

• Use VADER Sentiment Analyzer (nltk.sentiment.vader).
• It gives polarity scores (positive, negative, neutral, compound).
• Classify based on compound score.

6. Output Results

• For each review, print:
  • Review text
  • Classification (Positive/Negative/Neutral)
  • Confidence score (absolute polarity value)

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