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Deriving Sentiments from Opinions or Product Choices

See how to:

  • Quantify positive or negative sentiment in unstructured text data
  • Understand basics of Lexical Sentiment Analysis
    • Scores sentiment from individual words in each doc when no external measure of sentiment is available
    • Uses a Sentiment dictionary (aka "lexicon") that specifies scores (e.g., wonderful = +90, disappointed = -30)
    • Scores individual sentiment phrase to calculate document sentiment
    • Create three dictionary lists - Sentiment term: a term bearing sentiment such as ‘wonderful’; Intensifier term: sentiment multiplier such as ‘very’; Negator term: sentiment polarity reversal such as not
      • Customize dictionaries for your situation, domain, and add scores
      • Import and export dictionaries
    • Understand scoring
      • Individual phrases are scored: Sentiment term score  x  intensifier weighting  x  negation (-1)
      • Documents are scored: Scaled: mean sentiment phrase score and Min/Max: Sum of max and min sentiment
    • Understand rules
      • A term can belong to only one class: sentiment, intensifier, or negator
      • Parse Documents option uses natural language processing (NLP) to better assign negators and intensifiers
      • Stemming does not affect sentiment scores (and for good reason)
      • If a previously declared stop word is added to the dictionary, it is temporarily removed as a stop word in every analysis within the Text Explorer window
    • Explore results
      • Sentiment Distribution provides overall view of sentiment across all docs
      • Sentiment Term List shows most frequent sentiment terms are most frequent and you can click to browse all documents with selected term(s)
      • Document Table presents document-level scoring (hover for explanations)
      • Text pane shows terms in context (hover for explanations)
      • Features section finds terms that frequently co-occur with sentiment terms and rescores documents with respect to selected features
    • Export sentiment scores for use in other analyses                                                       
  • Understand basics of Derived Sentiment Analysis
    • Uses external sentiment measure (e.g. satisfaction rating) to drive sentiment analysis
    • Uses Text Explorer's Term Selection to build a model of sentiment measure predicted by document-term matrix (DTM)
    • Uses data to derive term, document sentiment scores
    • Identifies sentiment terms we may otherwise miss
    • Model can be applied to future text, even when external sentiment measure is not available
  • Run the Derived Sentiment Analysis using Term Selection
    • Use nominal, ordinal, or continuous response variables
      • Nominal with >2 levels requires choosing target class (i.e. reduces to binary outcome of target vs not-target)
      • Ordinal must be numeric data type, is modeled as continuous response with normal distribution
    • Select additional up-front options: Estimation and validation methods, DTM weighting, Max terms in DTM)
    • Access Generalized Regression controls after running initial model and build multiple models to find the best
  • Explore results
    • Top-level summary is similar to Sentiment Analysis
    • ‘Contribution’ is generic word for response scale (e.g., log odds)
    • Document's contribution values are sum of term coefficients (regression model coefficient)
    • Term LogWorth is function of p-value (higher LogWorth corresponds to lower p-value)

Resources

Sentiment SummarySentiment Summary

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