Taking the world s pulse

Transcription

1 Taking the world s pulse Antal van den Bosch Language Machines, Centre for Language Studies With Florian Kunneman, Ali Hürriyetoglu, Iris Hendrickx ICT.Open 2015, 24 March 2015

2 Language Machines

3 5x10 9 Est. number Estimated Est. of number Est. web number pages no. of web of of web pages web pages 0 555x10 billion x10 50 billion x10 454x10 billion 5x x10 4.5x10 404x10 billion x10 353x10 4x10 billion x10 3.5x10 303x10 billion x10 252x10 3x10 billion x10 2.5x10 202x10 billion x10 151x10 billion 2x x10 101x10 5x10 billion Google Bing Google Bing Google Bing 15 Caffeine update (#14) Year Panda 1.0 update (#20) Panda 4.0 update (#31) x10 5x10 billion x Launch of Launch of Catapult Bing 2009 (#9) BingBot update crawler (#18) (#33) Year Year Van den Bosch, Bogers, and De Kunder. A Longitudinal Analysis of Search Engine Index Size. To appear in Proceedings of ISSI-2015 Year

4 COMMIT/ TWIQS.NL We continuously search for common Dutch words on Twitter (keyword tracking) Additionally we collect all the tweets of most frequent 5000 users in Dutch of the previous month (user tracking) This generates close to 4 million tweets per day

5 COMMIT/ TWIQS.NL ETEN

6 COMMIT/ TWIQS.NL HEDDE

7 COMMIT/ PROVINCIAL ELECTIONS MARCH 2015

8 COMMIT/ EXAMS IN DREAM TEXTS

9 Looking forward or looking backward to an event

10 The language of time: data-driven time estimates

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13 Mean Absolute Error relative to event time Results Table, figure

14 Lama Events (

15 Hürriyetoglu, A., Kunneman, F, and Van den Bosch, A. (2013). Estimating the time between Twitter messages and future events. In Proceedings of the 13th Dutch-Belgian Information Retrieval Workshop, pp Hürriyetoglu, A., Oostdijk, N., and Van den Bosch, A. (2014). Estimating time to event from tweets using temporal expressions. In Proceedings of the Workshop on Language Analysis in Social Media, LASM-2014, Gothenburg, Sweden, pp ACL. Kunneman, F., and Van den Bosch, A. (2012). Leveraging unscheduled event prediction through mining scheduled event tweets. In N. Roos, M. Winands, and J. Uiterwijk (Eds.), Proceedings of the 24th Benelux Conference on Artficial Intelligence, Maastricht, The Netherlands, pp Kunneman, F., Hürriyetoglu, A., Oostdijk, N., and Van den Bosch, A. (2014). Timely identification of event start dates from Twitter. Computational Linguistics in the Netherlands Journal, 4, pp Kunneman, F., Liebrecht, C., Van den Bosch, A., and Van Mulken, M. (2014). Signaling sarcasm: From hyperbole to hashtag. Information Processing & Management, published online. Kunneman, F., Liebrecht, C., and Van den Bosch, A. (2014). The (un)predictability of emotional hashtags in Twitter. In Proceedings of the Workshop on Language Analysis in Social Media, LASM-2014, Gothenburg, Sweden, pp ACL. Kunneman, F., and Van den Bosch, A. (2014). Event detection in Twitter: A machine learning approach based on term pivoting. In F. Grootjen, M. Otworowska, and J. Kwisthout (Eds.), Proceedings of the 26th Benelux Conference on Artificial Intelligence, pp Liebrecht, C., Kunneman, F., and Van den Bosch, A. (2013). The perfect solution for detecting sarcasm in tweets #not. In A. Balahur, E. van der Goot, and A. Montoyo (Eds.), Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA-2013, pp New Brunswick, NJ: ACL. Tops, H., Kunneman, F., and Van den Bosch, A. (2013). Predicting time-to-event from Twitter messages. In K. Hindriks, M. de Weerdt, B. van Riemsdijk, and M. Warnier (Eds.), Proceedings of the 25th Benelux Artificial Intelligence Conference, BNAIC Delft, The Netherlands.

16 Information need Words, phrases Search, translate Linked data NLP Bags of words Classify, filter, recommend Text Text Text

17 Information need Named entities: LHO, JFK Lee Harvey Oswald may not have been acting alone in the assassination of John F. Kennedy Semantic roles: LHO actor, JFK patient Negator: not; negated:? Text Text Text Assassination plot Lee Harvey Oswald may not have been acting alone in the assassination of John F. Kennedy Conspiracy theory

18 Information vs. language Every little fact can be (and is) expressed in an endless variety of linguistic expressions Language is a great way of hiding information Navajo Code Talkers Henry Bake and George Kirk, 12/1943 (ARC )

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20 Text mining of dream descriptions Dreambank ( - archive of personal dream descriptions collected in different studies - 61 collections (approx. 30K dreams) 143 dreams of blind dreamers 469 dreams of college women, late 1940s Phil, teens: 106 dreams; late 20s: 219 dreams; retirement: 180 dreams 490 female, 491 male dreams annotated with Hall & Van de Castle norms LDA (Latent Dirichlet Allocation) - 50 topics iterations, Gibbs sampling - Look for significant differences (log-likelihood test, G 2 ) in topic distributions in female vs male dreams

21 Generic dream topics store money buy get pay bill man grocery lot counter bank shopping machine give tickets shop put dollars change bought bathroom toilet hair shower water go room bath floor clean wash see naked sink tub pee bathtub face cut towel book paper read books find picture write writing reading letter pictures written name looking look something library letters office computer

22 Dream topics, women (significant) house mother father baby old boy brother family girl home little sister children years parents child see aunt son kids church wedding people married front sit seats seat aisle back table sitting ceremony group place priest getting side room chair wearing white dress black blue red clothes hair dressed shirt put shoes wear pair pants hat green suit see old play dance stage music audience group playing song people dancing singing show sing part piano good band performance do concert pool swimming water swim go board end burt dive diving bottom little bathing side deep suit lake watching shallow underwater

23 Dream topics, men (significant) car driving drive road truck get going go seat side back cars stop front parked turn driver street station parking game ball playing team play basketball football field high baseball coach good balls hit players tennis school man other player gun men man shoot shot people fire kill shooting guns police war run killed enemy escape being soldiers fight get building stairs floor go get elevator people going door top hall room roof wall high office find steps way ladder

24 Can we predict hashtag usage?

25 Distant supervision for emotion detection in Twitter Leveraging uncontrolled labeling to obtain large amounts of training data Two forms of emotion hashtag usage: - Meta-communication - Add emotion

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27 Hashtags conveying sarcasm #sarcasm #irony #cynicism #not 90 % of tweets with selected hashtag are indeed sarcastic (Cohen s Kappa: 0.44)

28 1: Sarcastic tweets 2: Random tweets: 406,439 3: Day of tweets February ,246,904 tweets 353 with sarcastic hashtag

29 Results

30 Analysis: tweets without sarcasm hashtag 250 top ranked tweets without sarcasm hashtag by classifier confidence for sarcasm Three annotators Is a tweet sarcastic or not? (strict decision)

31 Analysis: tweets without sarcasm hashtag

32 What next? Profiling of Twitter users - Tweetgenie: age, gender - Levels of aggression, sarcasm Mood and emotion detection in events - Emotion classifiers - Beyond list-based sentiment analysis Predicting events of specific types - Warnings for disasters - Occupations - Illegal parties

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