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Intelligent Interactive Multimedia Systems and Services 2016 pp 609–618Cite as

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

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 55))

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.

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References

  1. D’Avanzo, E., Pilato, G.: Mining social network users opinions’ to aid buyers’ shopping decisions. Comput. Hum. Behav. 51, 1284–1294 (2014)

    Article  Google Scholar 

  2. 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)

    Google Scholar 

  3. Eckman, P.: An argument for basic emotions. Cogn. Emot. 6(3/4), 169–200 (1992)

    Article  Google Scholar 

  4. Ghag, K., Shah, K.: SentiTFIDF—sentiment classification using relative term frequency inverse document frequency. Int. J. Adv. Comput. Sci. Appl. Sci. Inf. Organ.

    Google Scholar 

  5. Jurka, T.P.: Tools for Sentiment Analysis, 01 Aug 2012

    Google Scholar 

  6. Liu, B.: Sentiment analysis and subjectivity. In: Indurkhya, N., Damerau, F.J.: Handbook of Natural Language Processing, pp. 627–665. CRC Press (2010)

    Google Scholar 

  7. 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)

    Google Scholar 

  8. 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)

    Google Scholar 

  9. Santorini, B.: Part-of-speech tagging guidelines for the Penn treebank project. In: D. o. Science, Technical reports. University of Pennsylvania (1995)

    Google Scholar 

  10. 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)

    Google Scholar 

  11. 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)

    Google Scholar 

  12. 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)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Scuola delle Scienze di Base e Applicate, University of Palermo, Palermo, Italy

    Diego Frias

  2. Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR (CNR), Palermo, Italy

    Giovanni Pilato

Authors
  1. Diego Frias
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  2. Giovanni Pilato
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Corresponding author

Correspondence to Giovanni Pilato .

Editor information

Editors and Affiliations

  1. Institute for High-Perfor Computing Netw, National Research Council of Italy, Naples, Italy

    Giuseppe De Pietro

  2. Institute for High-Perfo Computing Netwo, National Research Council of Italy, Naples, Italy

    Luigi Gallo

  3. KES International, Shoreham-by-sea, United Kingdom

    Robert J. Howlett

  4. University of Canberra, Canberra, South Australia, Australia

    Lakhmi C. Jain

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© 2016 Springer International Publishing Switzerland

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Frias, D., Pilato, G. (2016). A Data-Driven Approach to Dynamically Learn Focused Lexicons for Recognizing Emotions in Social Network Streams. In: Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2016. Smart Innovation, Systems and Technologies, vol 55. Springer, Cham. https://doi.org/10.1007/978-3-319-39345-2_54

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  • DOI: https://doi.org/10.1007/978-3-319-39345-2_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39344-5

  • Online ISBN: 978-3-319-39345-2

  • eBook Packages: EngineeringEngineering (R0)

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