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[SLIDE] Tecniche basate su machine learning per la determinazione del profilo di un autore su Twitter

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[SLIDE] Tecniche basate su machine learning per la determinazione del profilo di un autore su Twitter

  1. 1. Tecniche basate su machine learning per la determinazione del profilo di un autore su Twitter Tesi di Laurea Magistrale in Ing. Informatica Laureanda: A. Laderchi Correlatori: prof. E. Medvet dott. A. De Lorenzo dott. F. Tarlao Relatore: prof. A. Bartoli Anno Accademico 2014-2015
  2. 2. 14 ottobre 2015 Contesto Social media (blog, social network, ecc.): ● testi lunghi o brevi ● testi molto spesso informali Profilo di un autore: ● sesso ● età ● personalità Domande: ● si può determinare il profilo in base allo stile di scrittura? ● quali elementi del testo bisogna considerare? Introduzione 2 / 13
  3. 3. 14 ottobre 2015 Importanza ● E-commerce: ○ prodotti consigliati in base al profilo ○ interfacce adattabili ● Analisi forense: ○ profilo di un colpevole sulla base di tracce scritte ● Relazioni: ○ successo sentimentale ○ successo professionale ● Filtraggio di informazioni Introduzione 3 / 13
  4. 4. 14 ottobre 2015 Obbiettivo Sviluppo di un metodo in grado di risolvere il problema della determinazione del profilo di un autore (competizione PAN 2015). Caratteristiche: ● 4 lingue differenti (EN, ES, IT, NL) ● a partire da esempi → machine learning ○ documenti con attributi già associati ● valutazione finale del metodo su documenti non disponibili Introduzione 4 / 13
  5. 5. 14 ottobre 2015 5 / 13 Attributi da determinare Introduzione ● sesso ○ maschile ○ femminile ● fascia d’età (solo per EN ed ES) ○ 18-24 ○ 25-34 ○ 35-49 ○ 50+ ● 5 tratti della personalità ○ valori compresi tra -0.5 e +0.5 Problemi di classificazione Problemi di regressione Accuratezza RMSE (Root-Mean-Square Error)
  6. 6. 14 ottobre 2015 6 / 13 Tratti della personalità Introduzione Modello Big Five (OCEAN): ● Apertura mentale (Openness) ○ curiosità, intelligenza, immaginazione ● Coscienziosità (Conscientiousness) ○ responsabilità, organizzazione, perseveranza ● Estroversione (Extroversion) ○ espansività, fiducia, entusiasmo ● Amicalità (Agreableness) ○ cooperazione, disponibilità, educazione ● Nevroticismo (Neuroticism) ○ ansietà, insicurezza, suscettibilità
  7. 7. 14 ottobre 2015 7 / 13 Approccio Metodo utilizzato ● Definizione di numerose feature da estrarre dai documenti: ○ stilometriche (punteggiatura, emoticon, numeri, ecc.) ○ contenuto (famiglia, lavoro, sport, sentimenti, ecc.) ○ ibride (preposizioni, articoli, pronomi, blog-word, ecc.) ● Algoritmi di previsione (classificazione/regressione): ○ SVM (Support Vector Machine) ○ Random Forest ● Metodo di validazione: ○ leave-one-out
  8. 8. 14 ottobre 2015 8 / 13 Selezione delle feature Metodo utilizzato 26 problemi da risolvere: ● (1 attributo x 2 lingue) + (6 attributi x 4 lingue) Per ogni problema: ● circa 40 sottoinsiemi di feature ● SVM e Random Forest Sui documenti disponibili: ● configurazioni feature-algoritmo migliori scelte in base a: ○ accuratezza (sesso, fascia d’età) ○ RMSE (tratti personali)
  9. 9. 14 ottobre 2015 9 / 13 Risultati finali PAN 2015 Sesso e fascia d’età Tratti personali Lingua Sesso Età Joint RMSE Estro Nevro Amica Consc Apert Glob EN 0.6479 0.7465 0.4718 0.1605 0.1480 0.2323 0.1360 0.1418 0.1445 0.6557 ES 0.8523 0.4205 0.3295 0.1562 0.1701 0.1867 0.1463 0.1302 0.1459 0.5867 IT 0.5000 - 0.5000 0.1405 0.1004 0.1889 0.1386 0.1298 0.1450 0.6797 NL 0.7188 - 0.7188 0.1156 0.1467 0.1393 0.1261 0.0962 0.0696 0.8016 Punteggio finale 0.6809 In grassetto: i valori migliori rispetto alla media dei risultati di tutti i partecipanti.
  10. 10. 14 ottobre 2015 10 / 13 Classifica finale PAN 2015 Sesso e fascia d’età Tratti personali Lingua Sesso Età Joint RMSE Estro Nevro Amica Consc Apert Glob EN 16/22 6/22 15/22 8/22 10/22 13/22 3/22 8/22 11/22 14/22 ES 7/21 20/21 19/21 5/21 8/21 4/21 9/21 3/21 9/21 19/21 IT 17/19 - 17/19 6/19 5/19 4/19 8/19 8/19 5/19 15/19 NL 10/21 - 10/21 4/21 8/21 6/21 7/21 2/21 6/21 9/21 Posizione finale 14/22 In grassetto: le posizioni risultanti tra le migliori 10.
  11. 11. 14 ottobre 2015 11 / 13 Criticità della competizione PAN 2015 Scoperta la possibilità di: ● conoscere parte dei documenti “segreti” usati per la valutazione finale ● prevedere le risposte corrette a tutti (o quasi) i problemi tramite: ○ ricerca in rete di tweet ○ incrocio ID autori ● falsare la competizione in modo radicale
  12. 12. 14 ottobre 2015 12 / 13 Conclusioni Realizzato un metodo per la determinazione del profilo di un autore: ● buoni risultati: ○ nella previsione dei tratti personali ○ soprattutto nella lingua olandese Riguardo alle criticità: ● tempestiva notifica agli organizzatori: ○ hanno corretto i dati e ringraziato pubblicamente il nostro contributo
  13. 13. 14 ottobre 2015 Fine Grazie per l’attenzione! 13 / 13
  14. 14. 14 ottobre 2015 Approfondimento risultati finali PAN 2015 * accuratezza ** RMSE *** Accuratezza: ● 0 → nessuna previsione corretta ● 1 → tutte previsioni corrette RMSE: ● 0 → tutte previsioni corrette In corsivo: valori medi considerando tutti i partecipanti. In grassetto: valori superiori alla media di tutti i partecipanti. Sesso e fascia d’età* Tratti personali** Lingua Sesso Età Joint RMSE Estro Nevro Amica Consc Apert Glob*** EN 0.6479 0.7465 0.4718 0.1605 0.1480 0.2323 0.1360 0.1418 0.1445 0.6557 0.7132 0.6863 0.5086 0.1763 0.1629 0.2379 0.1612 0.1586 0.1607 0.6662 ES 0.8523 0.4205 0.3295 0.1562 0.1701 0.1867 0.1463 0.1302 0.1459 0.5867 0.7944 0.6223 0.5216 0.1775 0.1845 0.2174 0.1579 0.1689 0.1588 0.6721 IT 0.5000 - 0.5000 0.1405 0.1004 0.1889 0.1386 0.1298 0.1450 0.6797 0.6418 - 0.6418 0.1602 0.1249 0.2088 0.1415 0.1502 0.1756 0.7408 NL 0.7188 - 0.7188 0.1156 0.1467 0.1393 0.1261 0.0962 0.0696 0.8016 0.7032 - 0.7032 0.1422 0.1503 0.1664 0.1435 0.1448 0.1062 0.7805 Punteggio finale 0.6809 0.7171

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