Polarity is a numerical orientation of sentiment: negative, positive, or neutral. It helps turn customer-review text into a measurable signal for product and business analysis. section 7.15
A practical polarity workflow includes:
- Prepare review text by lowercasing, removing punctuation, filtering stop words and frequent words, tokenizing, and lemmatizing. section 7.15
- Apply TextBlob sentiment analysis to obtain polarity, optionally retaining subjectivity alongside it. section 7.15
- Map polarity scores to labels such as Extremely Satisfied, Neutral, Bad, or Extremely Dis-satisfied, then compare the results with star ratings. section 7.15
Customer-review analysis
After collecting product reviews and cleaning their bodies, apply TextBlob to each review, save the polarity scores, plot label frequencies, and merge the scores with the original review data. Comparing polarity with star ratings can reveal that ratings do not fully represent expressed sentiment. section 7.15
# the functions generates polarity and subjectivity here, subsetting the polarity only here allReviewsSentiment = reviewData.body[:900].apply(lambda x: TextBlob(x).sentiment[0]) # this contains boths subjectivity and polarity allReviewsSentimentComplete = reviewData.body[:900].apply(lambda x: TextBlob(x).sentiment) allReviewsSentimentComplete.head()
Inspect the Python expression that extracts TextBlob polarity for each review and optionally returns complete sentiment results. section 7.15
