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1/20 Final talk Automatically Acquiring a Dictionary of Emotion- Provoking Events Student: Hoa Vu-Trong – VNU Supervisor: Graham sensei - NAIST.

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Presentation on theme: "1/20 Final talk Automatically Acquiring a Dictionary of Emotion- Provoking Events Student: Hoa Vu-Trong – VNU Supervisor: Graham sensei - NAIST."— Presentation transcript:

1 1/20 Final talk Automatically Acquiring a Dictionary of Emotion- Provoking Events Student: Hoa Vu-Trong – VNU Supervisor: Graham sensei - NAIST

2 2/20 Can Twitter benefit a dialogue system? Dialog System Machine: Hello! User: Hello! User: A guy next to me today, are too noisy ! Machine: That's so annoying! User: Twitter users

3 3/20 Motivation ● Emotion is not present in specific word. ● 4% of words imply emotion [1] [1] Pennebaker, J.W., Mehl, M.R., Niederhoffer, K.: Psychological aspects of natural language use: Our words, our selves. Annual Review of Psychology 54, 547–577 (2003) ● Text emotion classifier Simple architecture of dialogue system with emotion adaption. 1. I feel happy today 2. I met my friend today

4 4/20 Motivation Arbitrarily large set of emotion-provoking events can be collected from Twitter You must be very happy 400M tweets/day

5 5/20 Method ● Emotion and Event have relation. ● Pattern learning is an effective way to harvest semantic relation – Espresso (Pantel and Pennacchiotti 06). Ex: “I'm happy that I have the support of my friends. I love all of them!” “I'm sad that tomorrow is Monday and I have to work. It's bad day” Pattern: I be EMOTION that EVENT Instances: happy – I have the support of my friends sad – tomorrow is Monday and I have to work

6 6/20 Espresso Algorithm ● Used in mining semantic relation (eg: is-a, has-a …) begins with some seed instances. ● Each iteration contains 3 phases: – Pattern Induction – Pattern ranking – Instance extraction ● Stopping criterion: enough patterns, average reliabilty of the patterns decrease t% or exeeds defined number of iterations.

7 7/20 Espresso Algorithm ● Pattern Induction: Infers all the patterns P that connect the seed instances. Ex: I be EMOTION that EVENT. I love all of you I be EMOTION that EVENT. It be bad day I be EMOTION that EVENT - 2 times EMOTION that EVENT. - 2 times EMOTION that EVENT. I love all – 1 time … I'm happy that I have the support of my friends. I love all of them! I'm sad that tomorrow is Monday and I have to work. It's bad day

8 8/20 Espresso Algorithm ● Pattern ranking: Rank all the patterns and extract top K reliable ones. ● Reliable patterns: one that both highly precise and one that extract many instances (more in next slides).

9 9/20 Espresso Algorithm ● Instance Extraction: Retrieves top M reliable instances match K patterns extracted from previous phase. ● Reliable instance: one that highly associated with as many reliable patterns. (more in next slides)

10 10/20 Espresso Algorithm ● Strength of association between instance i(x,y) and pattern p is measured by PMI.

11 11/20 Espresso Algorithm ● Pattern reliability: ● Instance reliability:

12 Grouping events ● Relieve sparsity issues to some extent by sharing statistics among the events in a single group ● allows humans to understand the events better, highlighting the important events shared by many people ● Using hierarchical agglomerative clustering and the single-linkage criterion using cosine similarity as a distance measure

13 13/20 Experiments ● Data corpus: 30 million tweets from Neubig and Duh 13' [1] ● Tweet normalization by Han et al 12' [2] ● Stanford parser [3] was employed to make sure that event must be a sentence [1] Graham Neubig, Kevin Duh.How Much is Said in a Tweet? A Multilingual, Information-theoretic Perspective in AAAI Spring Symposium on Analyzing Microtext. Stanford, California. March [2] Han et al. Automatically Constructing a Normalisation Dictionary for Microblogs in EMLNP 2012

14 14/20 Experiments ● 6 basic emotion classes defined by Ekman [1] : – Anger: angry, mad – Digust: digusted, terrible – Fear: afraid, scared – Happiness: happy, glad – Sadness: sad, upset – Surprise: surprised, astonished [1]Ekman, P.: Universals and cultural dierences in facial expressions of emotions. Nebraska Symposium on Motivation 19, 207{283 (1972)}

15 15/20 Experiments ● We start the system with the seed instances collected by the pattern: “I be EMOTION that EVENT” ● Reliability of seed instances is 1. ● Stopping criterion: limit iterations.

16 16/20 Result ● Happiness: events ● Sadness: 3909 events ● Fear: 8798 events ● Anger: 2133 events ● Surprise: 2466 events ● Disgust: 26 events

17 17/20 Result ● Some new patterns: I feel EMOTION when EVENT I be EMOTION because EVENT I be EMOTION EVENT I get so EMOTION when EVENT Make me EMOTION when EVENT Get really EMOTION that EVENT Be really EMOTION to hear that EVENT Be EMOTION to know that EVENT EMOTION at the fact that EVENT be EMOTION to death that EVENT …

18 18/20 Evaluation ● Using Mean Reciprocal Rank(MMR): PredictedHuman annotation RankReciprocal rank SurprisedHappiness Surprise Sadness 21/2

19 19/20 Evaluation ● Measuring recall – Asking 30 people about 5 events that provoke each of five emotions EmotionsEvents happinessmeeting friendsbuying/getting something I want going on a date sadnessa plan gets cancelledsomeone dies/gets sick failing a test angersomeone breaks a promise someone insults mesomeone breaks something of mine feargetting a sudden phone call seeing an insectwalking at night surpriseseeing a friend unexpectedly seeing a car suddenly appear hearing a loud noise

20 20/20 Evaluation ● Evaluation emotion-provoking events ● Human evaluation on top 100 groups. MethodsMRRRecall Seed Seed + clustering Espresso Espresso + clustering EmotionsMRRRecall Happiness Sadness Anger fear Surprise

21 21/20 Disscusion ● Recall is still relatively low ● Events extracted from Twitter were somewhat biased towards everyday events or events regarding love and dating ● for surprise we didn’t manage to extract any of the emotions created by the annotators at all

22 22/20 In Conclusion ● This work focus on acquiring emotion-provoking events ● Using Espresso algorithm to learn patterns and extract events then similar events are grouped to create a dictionary. ● Paper summited to EACL 2014

23 23/20 Arigato gozaimasu

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