From Sentiment to Emotion Analysis in Social Networks

1. From Sentiment to Emotion Analysis in Social Networks Jie Tang Department of Computer Science and Technology Tsinghua University, China

2. From Info. Space to Social Space Revolutionary changes… Info. Space 1.Social Tie & Group 2.Social Influence 3.Collective Intelligence Interaction Social Space

3. Revolutionary Changes Social Networks • The Web knows you than yourself: • Contextual computing • Big data marketing O2O More … ... • HumanComputation: • CAPTCHA + OCR • MOOC • Duolingo (Machine Translation) Education • Embedding social in search: • Google plus • FB graph search • Bing’s influence Search

4. Let us start with sentiment analysis…

5. “Love Obama” I hate Obama, the worst president ever I love Obama Obama is fantastic Obama is great! No Obama in 2012! He cannot be the next president! Positive Negative

6. Homophily • Homophily—“birds of a feather flock together” • A user in the social network tends to be similar to their connected neighbors. • Originated from different mechanisms • Influence • Indicates people tend to follow the behaviors of their friends • Selection • Indicates people tend to create relationships with other people who are already similar to them • Confounding variables • Other unknown variables exist, which may cause friends to behave similarly with one another.

7. Twitter Data • Twitter • 1,414,340 users and 480,435,500 tweets • 274,644,047 t-followedges and 58,387,964@ edges [1] ChenhaoTan, Lillian Lee, Jie Tang, Long Jiang, Ming Zhou, and Ping Li. User-level sentiment analysis incorporating social networks. In KDD’11, pages 1397–1405, 2011.

8. Influence Shared sentiment conditioned on type of connection.

9. Selection Connectedness conditioned on labels

10. One question: what drives users’ sentiments?

11. Sentiment vs. Emotion Charles Darwin: • Emotion serves as a purpose for humans in aiding their survival during the evolution.[1] Emotion is the driving force of user’s sentiments… Emotion stimulates the mind 3000 times quicker than rational thought! [1] Charles Darwin.The Expression of Emotions in Man and Animals. John Murray, 1872.

12. Happy System SMS & Calling Location Activities Emotion Can we predict users’ emotion?

13. Observations (cont.) Dorm The Old Summer Palace ? ? ? ? GYM Activity correlation Classroom ? Location correlation (Red-happy) Karaoke

14. Observations (a) Implicit groups by emotions (a) Social correlation (c) Calling (SMS) correlation

15. Observations (cont.) Temporal correlation

16. Methodologies

17. MoodCast: Dynamic Continuous Factor Graph Model Our solution 1. We directly define continuous feature function; 2. Use Metropolis-Hasting algorithm to learn the factor graph model. • [1] Jie Tang, Yuan Zhang, Jimeng Sun, JinghaiRao, Wenjing Yu, Yiran Chen, and ACM Fong. Quantitative Study of Individual Emotional States in Social Networks. IEEE TAC, 2012, Volume 3, Issue 2, Pages 132-144.

18. Problem Formulation Gt =(V, Et, Xt, Yt) Time t Emotion: Sad Time t-1, t-2… Attributes: - Location: Lab - Activity: Working Learning Task:

19. Dynamic Continuous Factor Graph Model Time t’ Time t : Binary function

20. Learning with Factor Graphs y5 y4 y3 y'3 y1 y2 Attribute Social Temporal

21. MH-based Learning algorithm Random Sampling Update • [1] Yuan Zhang, Jie Tang, Jimeng Sun, Yiran Chen, and JinghaiRao. MoodCast: Emotion Prediction via Dynamic Continuous Factor Graph Model. In ICDM’10. pp. 1193-1198.

22. Still Challenges • Q1: Are there any other social factor that may affect the prediction results? • Q2: How to scale up the model to large networks?

23. Q1: Conformity Influence Positive Negative I love Obama 3. Group conformity Obama is fantastic Obama is great! 1. Peer influence 2. Individual [1] Jie Tang, Sen Wu, and Jimeng Sun. Confluence: Conformity Influence in Large Social Networks. In KDD’13, 2013.

24. Conformity Factors • Individual conformity • Peer conformity • Group conformity A specific action performed by user v at time t All actions by user v

25. Q2: Distributed Learning Master Global update Slave Compute local gradient via random sampling Graph Partition by Metis Master-Slave Computing Inevitable loss of correlation factors!

26. Random Factor Graphs Master: Optimize with Gradient Descent Slave: Distributedly compute Gradient via LBP Gradients Parameters Master-Slave Computing

27. Model Inference • Calculate marginal probability in each subgraph • Aggregate the marginal probability and normalize

28. Theoretical Analysis • Θ*: Optional parameter of the complete graph • Θ: Optional parameter of the subgraphs • Ps,j: True marginal distributions on the complete graph • G*s,j: True marginal distributions on subgraphs • Let Es,j = logG*s,j– logPs,j，we have:

29. Experiments

30. Results for Sentiment Analysis • Twitter • 1,414,340 users and 480,435,500 tweets • 274,644,047 t-followedges and 58,387,964@ edges • Baseline • SVM Vote • Measures • Accuracy and Macro F1

31. Performance

32. Results of Different Learning Algorithms

34. Results for Emotion Analysis • Data Set • Baseline • SVM • SVM with network features • Naïve Bayes • Naïve Bayes with network features • Evaluation Measure: Precision, Recall, F1-Measure

35. Performance Result

36. Factor Contributions • All factors are important for predicting user emotions Mobile

37. Online Applications: Emotion Analysis on Flickr

39. Framework: Images -Aesthetic Effects -Emotions • Model: Factor Graphs for images in Social Networks • [1] JiaJia, Sen Wu, Xiaohui Wang, Peiyun Hu, LianhongCai, and Jie Tang. Can We Understand van Gogh’s Mood? Learning to Infer Affects from Images in Social Networks. In ACM Multimedia. pp. 857-860. • [2 ] Grand Challenge 2nd Prize Award

40. App1: Emotion Distribution on Flickr VS During Thanksgiving holiday Before Thanksgiving 2011 100,000 Images from Flickr Happy, Cheerful, and Peaceful

41. App2: Modify Images with Emotional Words Summer? Happy More than 180 different effects Autumn? Natural Original Image Winter? Clear

42. Summary

43. Summary • Social networks bring revolutionary changes to the Web and unprecedented opportunities for us • Emotion stimulates minds 3000 times faster than rational thoughts! • Embedding social theories into sentiment/emotion analysis can benefit many applications

44. Related Publications • ChenhaoTan, Lillian Lee, Jie Tang, Long Jiang, Ming Zhou, and Ping Li. User-level sentiment analysis incorporating social networks. In KDD’11, pages 1397–1405, 2011. • Jie Tang, Yuan Zhang, Jimeng Sun, JinghaiRao, Wenjing Yu, Yiran Chen, and ACM Fong. Quantitative Study of Individual Emotional States in Social Networks. IEEE Transactions on Affective Computing (TAC), 2012, Volume 3, Issue 2, Pages 132-144. (Selected as the Spotlight Paper) • Yuan Zhang, Jie Tang, Jimeng Sun, Yiran Chen, and JinghaiRao. MoodCast: Emotion Prediction via Dynamic Continuous Factor Graph Model. In ICDM’10. pp. 1193-1198. • JiaJia, SenWu, XiaohuiWang, PeiyunHu, LianhongCai, and Jie Tang. Can We Understand van Gogh’s Mood? Learning to Infer Affects from Images in Social Networks. In ACM MM, pages 857-860, 2012. • Xiaohui Wang, JiaJia, Peiyun Hu, Sen Wu, LianhongCai, and Jie Tang. Understanding the Emotional Impact of Images. (Grand Challenge)In ACM MM. pp. 1369-1370. (Grand Challenge 2nd Prize Award)

45. Thanks you！ Collaborators:Lillian Lee, Chenhao Tan (Cornell) Ming Zhou, Long Jiang (Microsoft), Yuan Zhang (MIT) Jimeng Sun (IBM), JinghaiRao (Nokia) Sen Wu, JiaJia, Xiaohui Wang, Yiran Chen, WenjingYu(THU) • Jie Tang, KEG, Tsinghua U, http://keg.cs.tsinghua.edu.cn/jietang • Download data & Codes,http://arnetminer.org/download