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Differentiated Semantic Analysis in Lexical Affect Sensing

Differentiated Semantic Analysis in Lexical Affect Sensing. Alexander Osherenko, Elisabeth André University of Augsburg. Examples. What is the emotional meaning ( I am very happy vs. I am happy )? What is the emotional meaning ( I am very sad even if everybody is happy! )?.

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Differentiated Semantic Analysis in Lexical Affect Sensing

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  1. Differentiated Semantic Analysis in Lexical Affect Sensing Alexander Osherenko, Elisabeth André University of Augsburg

  2. Examples • What is the emotional meaning (I am very happy vs. I am happy)? • What is the emotional meaning (I am very sad even if everybody is happy!)?

  3. Research questions • Differentiated analysis • Significant elements

  4. Differentiated linking • Rules that consider affective and grammatical information • Different granularities of analysis, e.g. majority • Different strategies of analysis, e.g. first phrase

  5. Information sources • Affective sources • Emotion words (GI, WordNet-Affect, Levin) • Movie glossary • Grammatical sources • Means from linguistic literature (negations, intensifiers, superordinate/subordinate clauses) • Means from own corpus studies (interdependencies)

  6. Corpora • FWF (759 sentences - 3 classes) • Statistical: 37.2% • BMRC-S (1010 sentences - 3-, 5-classes) • Statistical: 52.34% - 3 classes; 30.81% - 5 classes

  7. System • Relies on deep parsing in the Stanford parser and matching in the SPIN parser • 4,527 rules for emotion words • 74 rules for primitive grammatical elements • 154 empirical grammatical rules

  8. Results • 47.64% classwise recall value (FWF) – 37.2% statistical • 54.65% classwise recall value (3-classes BMRC-S) or 32.79% classwise recall value (5-classes BMRC-S) - 52.34% vs. 30.81% statistical

  9. Discussion • Semantic vs. statistical • Majority, First phrase

  10. Future work • Generalization • Emotional phrases/idioms • Quantifiers (Few people were happy, Many people were happy) • Context (dynamically changing)

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