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Making Sense of
    Location-based Micro-posts
     using Stream Reasoning

Irene 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
BOTTARI Mobile Application
Augmented 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
Gathering microposts data
Crawling 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 followers




Making Sense of Location-based Micro-posts using Stream Reasoning      4        #MSM Workshop at ESWC 2011
Gathering microposts data




For 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
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
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
Ontology modelling

                                                      twd:following                   twd:follower




              sioc:UserAccount                                    twd:TwitterUser
              sioc:id(xsd:string)                           twd:screenName(xsd:string)


                                                                        twd:post
                                                                                                             twd:retweet
sioc: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
Querying Microposts Dynamics with
                         Stream Reasoning and SPARQL with probabilities
% find people similar to me which are nearby in an interesting POI

SELECT ?poi1 ?user (f:similarWithProbability(ex:Alice, ?user) AS ?p)
         % the user I'm looking for should be "similar" to me
FROM STREAM <http://bottari.kr/streamOftweets> [1h STEP 10m]
         % from the stream of microposts of last 10 minutes
WHERE {
  ?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 she's close to it)
  ?poi2 geo:lat ?lat2; geo:long ?long2 ; skos:subject ?category .
         % the other POI is of the same category
FILTER( (?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
Thanks for your attention! Any question?
  Making Sense of Location-based Micro-posts using Stream Reasoning
    Paper Authors: Irene Celino, Daniele Dell'Aglio, 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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Making Sense of Location-based Microposts using Stream Reasoning

  • 1. Making Sense of Location-based Micro-posts using Stream Reasoning Irene 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. BOTTARI Mobile Application Augmented 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.
  • 4. Gathering microposts data Crawling 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 followers Making Sense of Location-based Micro-posts using Stream Reasoning 4 #MSM Workshop at ESWC 2011
  • 5. Gathering microposts data For 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
  • 6. 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
  • 7. 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
  • 8. Ontology modelling twd:following twd:follower sioc:UserAccount twd:TwitterUser sioc:id(xsd:string) twd:screenName(xsd:string) twd:post twd:retweet sioc: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
  • 9. Querying Microposts Dynamics with Stream Reasoning and SPARQL with probabilities % find people similar to me which are nearby in an interesting POI SELECT ?poi1 ?user (f:similarWithProbability(ex:Alice, ?user) AS ?p) % the user I'm looking for should be "similar" to me FROM STREAM <http://bottari.kr/streamOftweets> [1h STEP 10m] % from the stream of microposts of last 10 minutes WHERE { ?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 she's close to it) ?poi2 geo:lat ?lat2; geo:long ?long2 ; skos:subject ?category . % the other POI is of the same category FILTER( (?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
  • 10. Thanks for your attention! Any question? Making Sense of Location-based Micro-posts using Stream Reasoning Paper Authors: Irene Celino, Daniele Dell'Aglio, 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