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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