1. Emotions from text: machine learning for text-based emotion prediction Cecilia Alm, Dan Roth, Richard Sproat UIUC, Illinois HLT/EMPNLP 2005

2. Objective Classify the emotional affinity of sentences in the narrative domain of children’s fairy tales From the perspective of the story characters

3. Application Text-to-Speech synthesis of fairy tales

4. Classification Task Experiment 1: Classify a sentence into Emotional or Neutral classes Experiment 2: Classify a sentence into Neutral, Positive Emotion or Negative Emotion classes

5. Corpus 1580 manually-annotated sentences from fairy tales Positive Emotions = {Happy, +Surprised} Negative Emotions = {rest} 90% training, 10% testing

6. Corpus Statistics

7. Classification Method SNoW classifier 10-fold cross-validation to tweak the parameters

8. Sentence Features 1.First sentence in story 2.Combinations of features (7+11) 3.Direct speech (quote) 4.Thematic story type (e.g. animal tales) 5.Special punctuation (e.g. ! and ?) 6.Complete upper-case word 7.Sentence length in words 8.Ranges of story progress (e.g. 90%-100%) 9.Percentages of JJ, N, V, RB 10.Verb count in sentence 11.Positive and negative word counts (Di Cico et al.) 12.WordNet emotion words (Fellbaum) 13.Interjections and affective words (Johnson-Laird and Oatley) 14.Content BOW: N, V, JJ, RB words by POS

9. Experiment 1: Neutral and Emotional P(Netural) – always predict neutral Sequencing – use the correct emotion classes of adjacent sentences as features Columns – two sets of paramters

10. Experiment 2: Neutral, Positive Emotion and Negative Emotion Positive Emotions = {Happy, +Surprised} Negative Emotions = {Angry, Disgusted, Fearful, Sad, -Surprised}

11. Cumulative Removal of Feature Groups

12. Conclusion Text-based emotion prediction