Opinion mining in social networks Corrado Monti Universit` a degli - PowerPoint PPT Presentation

Measuring phenomena on Twitter Ongoing work Opinion mining in social networks Corrado Monti Universit` a degli Studi di Milano Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 1

Measuring phenomena on Twitter Ongoing work Measuring phenomena on Twitter ◮ Sentiment analysis and textual classification can extract information from the huge amount of Twitter messages ◮ Links with real-world indicators were discovered 1 ◮ Ok, but can we predict elections with Twitter? ◮ (Quite obviously) not really. 2 1 O’Connor et al., From tweets to polls: Linking text sentiment to public opinion time series , ICWSM 11 (2010): 122-129. 2 Chung and Mustafaraj, Can collective sentiment expressed on twitter predict political elections? In 25th AAAI Conf. on AI, 2011. Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 2

Measuring phenomena on Twitter Ongoing work ◮ For which phenomena is this possible? ◮ Apparently economic trust is one of them 3 ◮ Can political disaffection in Italy be measured through massive tweet classification? ◮ It is a relevant phenomenon ◮ Lot of interest, academic (sociology) and not 3 Bollen, Mao, Pepe, Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena , ICWSM 2011 Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 3

Measuring phenomena on Twitter Ongoing work Text classification ◮ “political disaffection” → political topic, negative sentiment, presence of some keywords ◮ We had a training dataset of 28 ′ 340 labelled tweets ◮ We developed ad-hoc re-usable classification techniques ◮ We built robust classifiers, thanks to ontologies from DBpedia Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 4

Measuring phenomena on Twitter Ongoing work Experimental comparison Surveys ◮ Accepted way to measure collective sentiment ◮ We got fraction of italians that say they would not vote for any party ◮ One every ∼ 10 days in April-October 2012 Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 5

Measuring phenomena on Twitter Ongoing work Experimental comparison Surveys Tweet sample ◮ Accepted way to measure ◮ 35 ′ 882 ′ 423 tweet collective sentiment ◮ For each survey, we compute ◮ We got fraction of italians the ratio of disaffected tweet that say they would not vote volume over political tweet for any party volume from ∆ = 14 days ◮ One every ∼ 10 days in before April-October 2012 Giugno 2012 L M M G V S D 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 5

Measuring phenomena on Twitter Ongoing work Results Pearson correlation index for ∆ = 14 days → ρ = 0.7860 0.25 Twitter disaffection ratio � � Inefficacy indicator 0.011 0.20 0.01 Twitter disaffection ratio � 0.009 Inefficacy indicator � � 0.008 0.15 � � � � � � � 0.007 � � � � � � 0.006 � 0.10 � � � 0.005 � � � � � � � � 0.004 � � � � 0.05 0.00 Apr 15 May 01 May 15 Jun 01 Jun 15 Jul 01 Jul 15 Aug 01 Aug 15 Sep 01 Sep 15 Oct 01 Time Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 6

Measuring phenomena on Twitter Ongoing work Interpretation ◮ Data seem to indicate a good correlation between disaffected tweet and diffusion of the phenomena in society ◮ This does not mean that Twitter is a representative sample! ◮ We can guess that the quantity of discussion about this pheonomenon is connected with how much it will spread Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 7

Measuring phenomena on Twitter Ongoing work We found peak causes from newspaper titles through text mining Attentato di Brindisi 0.020 Twitter disaffection ratio Rapporto di tweet (Teorie del complotto su Inefficacy indicator Sondaggi (Ine ffj cacia) stragismo di Stato) Scandalo Lega Twitter disaffection ratio 0.015 0.2 Inefficacy indicator S T S Amministrative o 0.175 w o n e n d 0.15 e d Scandalo a t 0.010 0.125 a Fiorito g g g 0.1 g i i 0.075 0.05 0.005 0.025 0. 0.000 Apr 01 Apr 15 May 01 May 15 Jun 01 Jun 15 Jul 01 Jul 15 Aug 01 Aug 15 Sep 01 Sep 15 Oct 01 Tempo Time Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 8

Measuring phenomena on Twitter Ongoing work Ongoing work ◮ I plan to use these kind of data to better understand network centrality measures ◮ We are developing a social network model where every node is represented as a set of features ◮ Features can be also be opinions! Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 9

Measuring phenomena on Twitter Ongoing work ◮ In this model, every node has a priori ability to transmit feature ◮ We are more or less able to reconstruct the value of this ability through Gibbs Sampling R 10 1 0.1 node 0 200 400 600 800 1000 Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 10

Measuring phenomena on Twitter Ongoing work Credits and References ◮ Corrado Monti, Matteo Zignani, Alessandro Rozza, Adam Arvidsson, Giovanni Zappella, and Elanor Colleoni. Modelling political disaffection from twitter data , Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining, p. 3. ACM, 2013. ◮ My supervisors are Paolo Boldi and Sebastiano Vigna ◮ Ongoing work with Irene Crimaldi (IMT Lucca) Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 11

Measuring phenomena on Twitter Ongoing work Thanks! email: corrado.monti@unimi.it Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 12