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Lexical Affect Sensing: Are Affect Dictionaries Necessary to Analyze Affect?

Lexical Affect Sensing: Are Affect Dictionaries Necessary to Analyze Affect?. Alexander Osherenko, Elisabeth André University of Augsburg. What emotions convey these textual utterances (SAL corpus)?. High arousal, negative valence: No, well , I'm not a fool .

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Lexical Affect Sensing: Are Affect Dictionaries Necessary to Analyze Affect?

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  1. Lexical Affect Sensing: Are Affect Dictionaries Necessary to Analyze Affect? Alexander Osherenko, Elisabeth André University of Augsburg

  2. What emotions conveythese textual utterances (SAL corpus)? • High arousal, negative valence:No, well, I'm not a fool. • High arousal, positive valence:No, <laugh> I think I'm being stupid actually. • Low arousal, positive valence:Yup.

  3. Dictionaries • Dictionary of Affect Language (DAL - Whissell) „happy” (evaluation, activation, imagery)3.0000 2.7500 2.2 • Linguistic Inquiry and Word Count Dictionary (LIWC) „happy” (categories)Affect, Positive emotion, Positive feeling • BNC frequency list11649 happy aj0 • SAL frequency list

  4. Research questions • Are recognition rates higher if word features are emotional? • Do emotive annotations in affect dictionaries improve recognition? • Are common words more useful than less common words? • Are dictionaries of affect more useful than general-purpose dictionaries?

  5. Feature Extraction and Evaluation • Word features • Selection of the most expressive words • Selection of the most frequent features • LIWC features (CAT-68 and CAT-8) • DAL features (EA-AVG) • Average values for the evaluation, activation, imagery scores

  6. Evaluation • 672 utterances from the SAL corpus as a 5-classes-problem • The majority vote strategy • The SVM classifier • Averaged recall value/number of word features

  7. Useful criterion of feature reduction without risking a severe degradation of recognition rates

  8. Do emotive annotations in affect dictionaries improve recognition? Affect-related features do not include discriminative information that is not yet included in the word counts

  9. Hard to say whether a reduction of features should be based rather on the frequency of words or their expressive qualities

  10. General-purpose dictionaries may provide similar results as affect dictionaries for similar numbers of features

  11. Recommendations • Frequency strategy is not worse than the emotional expressivity strategy • Similar trends for a movie reviews’ corpus • Results don‘t degrade dramatically when reducting number of word features (real-time recognition) • Acceptable results also with only affect annotations

  12. Thank you!

  13. Conclusion

  14. Mapping of FEELTRACE data onto affect segments • Examples: • [Affect segment: high_pos] (Laugh) I'm damn awful. How are you (laugh)? • [Affect segment: low_neg] Erm, that's probably true. 1.0 Activation high_neg high_pos neutral 0.2 Evaluation 1.0 low_neg low_pos

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