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Making Sense of Location-based Microposts using Stream Reasoning

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Making Sense of Location-based Microposts using Stream Reasoning

  1. 1. Making Sense of Location-based Micro-posts using Stream ReasoningIrene Celino, Daniele Dell’Aglio, Emanuele Della Valle, Yi Huang, Tony Lee, Stanley Park and Volker Tresp (CEFRIEL – Politecnico di Milano – Saltlux – SIEMENS) #MSM Making Sense of Microposts Workshop at ESWC 2011 – Heraklion, Crete, 30th May 2011
  2. 2. BOTTARI Mobile ApplicationAugmented Reality Application for Android to show POI information with their respective reputation to retrieve information on the basis of the geo-social context where can I find people nearby sharing my preferences? who shall I ask for an opinion on this restaurant?Making Sense of Location-based Micro-posts using Stream Reasoning 2 #MSM Workshop at ESWC 2011
  3. 3. Gathering microposts dataCrawling microposts User ranking model for adaptive crawling using users’ influence (ranking) to find appropriate and influential microposts in real-time Factors to compute ranking: Micropost frequencies # of mentioned or retweeted microposts Degree of interaction with followers and followings # of followersMaking Sense of Location-based Micro-posts using Stream Reasoning 4 #MSM Workshop at ESWC 2011
  4. 4. Gathering microposts dataFor now we’ve been crawling around 356,000,000 messages (5,300,000 messages / day) 1,100,000 users (14,000 users / day)Making Sense of Location-based Micro-posts using Stream Reasoning 5 #MSM Workshop at ESWC 2011
  5. 5. Sentiment Analysis – high-level view Sentiment analysis of microposts Compute "quantitative" ratings for each POI When possible, different ratings for different features of the POI (e.g., in case of restaurants: taste, service, price, …)Microposts about a specific Sentiment analysis Computed ratings Point of Interest algorithm (e.g. for restaurants) taste 7.8/10 service 4.2/10 price 6.0/10 Making Sense of Location-based Micro-posts using Stream Reasoning 6 #MSM Workshop at ESWC 2011
  6. 6. Sentiment Analysis – how it works Micropost message Precision tests: Auto-generated Yes Morphologically No rules ≈ 70% Analyzable? Manually-coded rules ≈ 90% Syllable kernel ≈ 50~60%Rule based Analysis Learned SVMs documents Auto generated rules Syllable Kernel Our target > 85% Reputations for each feature Making Sense of Location-based Micro-posts using Stream Reasoning 7 #MSM Workshop at ESWC 2011
  7. 7. Ontology modelling twd:following twd:follower sioc:UserAccount twd:TwitterUser sioc:id(xsd:string) twd:screenName(xsd:string) twd:post twd:retweetsioc:creator_of sioc:has_creator twd:discuss twd:reply twd:Tweet sioc:Post twd:messageID(xsd:string) sioc:content(xsd:string) twd:messageTimeStamp(xsd:string) twd:talksAboutPositively twd:talksAbout twd:talksAboutNeutrally twd:talksAboutNegatively geo:SpatialThing geo:NamedPlace Making Sense of Location-based Micro-posts using Stream Reasoning 8 #MSM Workshop at ESWC 2011
  8. 8. Querying Microposts Dynamics with Stream Reasoning and SPARQL with probabilities% find people similar to me which are nearby in an interesting POISELECT ?poi1 ?user (f:similarWithProbability(ex:Alice, ?user) AS ?p) % the user Im looking for should be "similar" to meFROM STREAM <http://bottari.kr/streamOftweets> [1h STEP 10m] % from the stream of microposts of last 10 minutesWHERE { ?user twd:post { twd:talksPositivelyAbout ?poi1 } . % target user tweeted positively about a POI ?poi1 geo:lat ?lat1; geo:long ?long1 ; skos:subject ?category . % this POI has a position and category ex:Alice twd:post { twd:talksAbout ?poi2 } . % current user tweeted about another POI (thus shes close to it) ?poi2 geo:lat ?lat2; geo:long ?long2 ; skos:subject ?category . % the other POI is of the same categoryFILTER( (?lat1-?lat2)<"0.1"^^xsd:float && (?lat1-?lat2)>"-0.1"^^xsd:float && (?long1-?long2)<"0.1"^^xsd:float && (?long1-?long2)>"-0.1"^^xsd:float ) % the target POI is close to the current user}ORDER BY DESC(?p)LIMIT 10 Making Sense of Location-based Micro-posts using Stream Reasoning 9 #MSM Workshop at ESWC 2011
  9. 9. Thanks for your attention! Any question? Making Sense of Location-based Micro-posts using Stream Reasoning Paper Authors: Irene Celino, Daniele DellAglio, Emanuele Della Valle, Yi Huang, Tony Lee, Stanley Park and Volker Tresp Contact: Irene Celino – Semantic Web Practice CEFRIEL – ICT Institute, Politecnico di Milano email: irene.celino@cefriel.it – web: http://swa.cefriel.it personal website: http://iricelino.org phone: +39-02-23954266 – fax: +39-02-23954466 slides available at: http://www.slideshare.net/iricelino #MSM Making Sense of Microposts Workshop at ESWC 2011 – Heraklion, Crete, 30th May 2011

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