Skip to main content

2016 | OriginalPaper | Buchkapitel

A Data-Driven Approach to Dynamically Learn Focused Lexicons for Recognizing Emotions in Social Network Streams

verfasst von : Diego Frias, Giovanni Pilato

Erschienen in: Intelligent Interactive Multimedia Systems and Services 2016

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Opinion Mining aims at identifying and classifying subjective information in a collection of documents. A variety of approach exists in literature, ranging from Supervised Learning to Unsupervised Learning. Currently, one of the biggest opinion resource of opinionated texts existing on the Web is represented by Social Networks. Networks are not only a vast collection of documents but they also represent a dynamic evolving resource as the users keep posting their own opinions. We based our work relying on this idea of dynamicity, building an evolving model that updates itself in real time as users submit their posts. This is done through a set of supervised techniques based on a Lexicon of emotionally-tagged terms (i.e. anger, disgust, fear, joy, sadness and surprise) that expands accordingly to user’s dynamic content.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat D’Avanzo, E., Pilato, G.: Mining social network users opinions’ to aid buyers’ shopping decisions. Comput. Hum. Behav. 51, 1284–1294 (2014)CrossRef D’Avanzo, E., Pilato, G.: Mining social network users opinions’ to aid buyers’ shopping decisions. Comput. Hum. Behav. 51, 1284–1294 (2014)CrossRef
2.
Zurück zum Zitat Dinu, L., Iuga, I.: The naive bayes classifier in opinion mining: in search of the best feature set. In: Computational Linguistics and Intelligent Text Processing: 13th International Conference, CICLing 2012, New Delhi, India, 11–17 Mar 2012, Proceedings, Part I, pp. 556–567. Springer, Berlin (2012) Dinu, L., Iuga, I.: The naive bayes classifier in opinion mining: in search of the best feature set. In: Computational Linguistics and Intelligent Text Processing: 13th International Conference, CICLing 2012, New Delhi, India, 11–17 Mar 2012, Proceedings, Part I, pp. 556–567. Springer, Berlin (2012)
3.
Zurück zum Zitat Eckman, P.: An argument for basic emotions. Cogn. Emot. 6(3/4), 169–200 (1992)CrossRef Eckman, P.: An argument for basic emotions. Cogn. Emot. 6(3/4), 169–200 (1992)CrossRef
4.
Zurück zum Zitat Ghag, K., Shah, K.: SentiTFIDF—sentiment classification using relative term frequency inverse document frequency. Int. J. Adv. Comput. Sci. Appl. Sci. Inf. Organ. Ghag, K., Shah, K.: SentiTFIDF—sentiment classification using relative term frequency inverse document frequency. Int. J. Adv. Comput. Sci. Appl. Sci. Inf. Organ.
5.
Zurück zum Zitat Jurka, T.P.: Tools for Sentiment Analysis, 01 Aug 2012 Jurka, T.P.: Tools for Sentiment Analysis, 01 Aug 2012
6.
Zurück zum Zitat Liu, B.: Sentiment analysis and subjectivity. In: Indurkhya, N., Damerau, F.J.: Handbook of Natural Language Processing, pp. 627–665. CRC Press (2010) Liu, B.: Sentiment analysis and subjectivity. In: Indurkhya, N., Damerau, F.J.: Handbook of Natural Language Processing, pp. 627–665. CRC Press (2010)
7.
Zurück zum Zitat Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing, pp. 443–447. Springer, London (1999) Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing, pp. 443–447. Springer, London (1999)
8.
Zurück zum Zitat Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning Techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002) Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning Techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)
9.
Zurück zum Zitat Santorini, B.: Part-of-speech tagging guidelines for the Penn treebank project. In: D. o. Science, Technical reports. University of Pennsylvania (1995) Santorini, B.: Part-of-speech tagging guidelines for the Penn treebank project. In: D. o. Science, Technical reports. University of Pennsylvania (1995)
10.
Zurück zum Zitat Terrana, D., Augello, A., Pilato, G.: Facebook users relationships analysis based on sentiment classification. In: Proceedings of 2014 IEEE International Conference on Semantic Computing (ICSC), pp. 290–296 (2014) Terrana, D., Augello, A., Pilato, G.: Facebook users relationships analysis based on sentiment classification. In: Proceedings of 2014 IEEE International Conference on Semantic Computing (ICSC), pp. 290–296 (2014)
11.
Zurück zum Zitat Wang, S., Manning, C.D.: Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of ACL’12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers, vol. 2, pp. 90–94 (2012) Wang, S., Manning, C.D.: Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of ACL’12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers, vol. 2, pp. 90–94 (2012)
12.
Zurück zum Zitat Strapparava, C., Valitutti, A.: WordNet-affect: an affective extension of WordNet. In: Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC 2004), Lisbon, pp. 1083–1086 (2004) Strapparava, C., Valitutti, A.: WordNet-affect: an affective extension of WordNet. In: Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC 2004), Lisbon, pp. 1083–1086 (2004)
Metadaten
Titel
A Data-Driven Approach to Dynamically Learn Focused Lexicons for Recognizing Emotions in Social Network Streams
verfasst von
Diego Frias
Giovanni Pilato
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-39345-2_54