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Subjectivity

As explained in Data Without Labels

Subjectivity is a numerical estimate of how much a text expresses opinion rather than factual information.

Subjectivity is a numerical estimate of how much text expresses opinion rather than factual information. A high score indicates more opinion than facts. section 7.15

Review analysis uses TextBlob sentiment output to obtain subjectivity alongside polarity, after lowercasing, stop-word removal, tokenization, and lemmatization. section 7.15

Customer-review analysis

Customer reviews are scraped, cleaned, scored, saved to CSV, and compared with star ratings to reveal differences between stated ratings and analyzed sentiment. section 7.15

How Data Without Labels covers it

How deep it goes
Defines opinion-versus-fact scoring and demonstrates extraction through preprocessing and TextBlob, without detailing the scoring internals.
Examples
Demonstrates subjectivity on scraped product reviews and compares analyzed sentiment with original customer star ratings.
What it assumes
Assumes basic Python data handling while demonstrating review loading, cleaning, tokenization, lemmatization, and sentiment extraction.
What you can do after
Apply subjectivity via TextBlob after preparing review text and merging sentiment results with the source dataset.
How it connects
Connects subjectivity with sentiment analysis, polarity, text preprocessing, customer reviews, and star ratings.
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