Pearl Pu - Emotion Detection In Social Media
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Air Transport IT Summit 2015 Industry Insight sessions
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Pearl Pu - Emotion Detection In Social Media
- 1. Emotion Detection in Social Media School of Computer and Communication Sciences EPFL Pearl Pu
- 3. Why Emotions? • They motivate us to take actions • They regulate decision and thought processes • They help us understand human behaviors http://nobaproject.com/modules/functions-of-emotions
- 4. What is emotion? ✓emotion is a reaction to events important to our organism ✓emotion leads to changes in multiple organismic subsystems Plutchik, The Nature of Emotions, 2001 Brave & Nass, Agents that care: Investigating the effects of orientation of emotion exhibited by an embodied computer agent, 2003
- 5. Emotion = coordinated changes in organismic subsystems 5 emotion components Cognitive (appraisal) Neurophysiological (bodily symptoms) Motivational or Behavioral (action tendencies) Motor expression (facial and vocal expressions) Subjective feeling or Affectiv (emotional experience)
- 6. rererere rere rerere rerere rere Ekman’s 6 emotions Ekman, An argument for basic emotions, 1992
- 7. rererere rere rerere rerere rere Plutchik Wheel Plutchik, The Nature of Emotions, 2001
- 9. Skin temperature Facial expression Gesture Voice Text How do we detect emotion?
- 10. Digimind Evolution (2003) http://www.digimind.com/ Digimind is a social media listening and monitoring tool leveraging on the results of emotion recognition in text. It was a B2B company based in Paris. What do we see in this particular example? On the upper left side: it’s comparing the different smart phones: how users discuss, perceive these brands. You can analyze topics and track competitors using this tool. You can monitor and analyze sub- topics within the larger topic. Drill down into details on what's happening, when it's happening, where, who's talking about you (your key influencers) and how (what's the sentiment).
- 12. BA C Emotional Categories Tweet frequency Emotion shape explore and investigate the full variety of elicited emotional reactions Our objective
- 13. 13 How does it work? Given a piece of text, detect automatically one or several words indicative of the respective emotions Possible output One emotion Distribution of emotionsSeveral emotions
- 14. Human Computation Task Emotion Label Happiness, Anger, Fear, No emotion… Emotion Strength Low, Medium, High
- 15. Constructed Lexicon OlympLex • 3193 terms with attached emotion distributions • Examples (per quadrant) Anger, Disgust, Scorn, … unfair, mad, ugh, annoyed, ticked off, idiots, slap, offended, epicfail, … Pride, Happiness, Interest, … bravo, champions, my girl, hero, woohoo, sohappy, good job, yessss, … ouch, noooo, eek, tough to watch, heartbroken, feel so bad, fearful, … Sadness, Fear, Pity, … astounded, luv u, incredible talent, omg, marry me, desiring, amaze, … Love, Surprise, Awe, …
- 16. Detecting emotion in social media helps us understand users’perception & attitudes Conclusion
- 17. DISCLAIMER Any use, republication or redistribution of this content is expressly prohibited without the prior written consent of the Author. Permission to copy and reproduce content may be granted by the author, at their discretion, and by request only. Source: presentation of Pearl Pu, EPFL at the 2015 SITA Air Transport IT Summit, Brussels. 2015 Air Transport IT Summit