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Emotional Concept Extraction Through Ontology-Enhanced Classification

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Metadata and Semantic Research (MTSR 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1057))

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Abstract

Capturing emotions affecting human behavior in social media bears strategic importance in many decision-making fields, such as business and public policy, health care, and financial services, or just social events. This paper introduces an emotion-based classification model to analyze the human behavior in reaction to some event described by a tweet trend. From tweets analysis, the model extracts terms expressing emotions, and then, it builds a topological space of emotion-based concepts. These concepts enable the training of the multi-class SVM classifier to identify emotions expressed in the tweets. Classifier results are “softly” interpreted as a blending of several emotional nuances which thoroughly depicts people’s feeling. An ontology model captures the emotional concepts returned by classification, with respect to the tweet trends. The associated knowledge base provides human behavior analysis, in response to an event, by a tweet trend, by SPARQL queries.

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Notes

  1. 1.

    http://www.psychpage.com/learning/library/assess/feelings.html.

  2. 2.

    https://wiki.dbpedia.org/.

  3. 3.

    https://www.w3.org/2004/02/skos/.

References

  1. AL-Sharuee, M.T., Liu, F., Pratama, M.: Sentiment analysis: an automatic contextual analysis and ensemble clustering approach and comparison. Data Knowl. Eng. 115, 194–213 (2018). https://doi.org/10.1016/j.datak.2018.04.001

    Article  Google Scholar 

  2. Alharbi, A.S.M., de Doncker, E.: Twitter sentiment analysis with a deep neural network: an enhanced approach using user behavioral information. Cogn. Syst. Res. 54, 50–61 (2019). https://doi.org/10.1016/j.cogsys.2018.10.001

    Article  Google Scholar 

  3. Çali, S., Balaman, Ş.Y.: Improved decisions for marketing, supply and purchasing: mining big data through an integration of sentiment analysis and intuitionistic fuzzy multi criteria assessment. Comput. Ind. Eng. 129, 315–332 (2019). https://doi.org/10.1016/j.cie.2019.01.051

    Article  Google Scholar 

  4. Cavaliere, D., Senatore, S., Loia, V.: Context-aware profiling of concepts from a semantic topological space. Knowl.-Based Syst. 130, 102–115 (2017). https://doi.org/10.1016/j.knosys.2017.05.008

    Article  Google Scholar 

  5. Cotelo, J., Cruz, F., Enríquez, F., Troyano, J.: Tweet categorization by combining content and structural knowledge. Inf. Fus. 31, 54–64 (2016). https://doi.org/10.1016/j.inffus.2016.01.002

    Article  Google Scholar 

  6. Hussein, D.M.E.D.M.: A survey on sentiment analysis challenges. J. King Saud Univ.-Eng. Sci. 30(4), 330–338 (2018). https://doi.org/10.1016/j.jksues.2016.04.002

    Article  Google Scholar 

  7. Li, X., Wu, C., Mai, F.: The effect of online reviews on product sales: a joint sentiment-topic analysis. Inf. Manag. 56(2), 172–184 (2019). https://doi.org/10.1016/j.im.2018.04.007, social Commerce and Social Media: Behaviors in the New Service Economy

    Article  Google Scholar 

  8. Öztürk, N., Ayvaz, S.: Sentiment analysis on Twitter: a text mining approach to the Syrian refugee crisis. Telemat. Inform. 35(1), 136–147 (2018). https://doi.org/10.1016/j.tele.2017.10.006

    Article  Google Scholar 

  9. Poria, S., Cambria, E., Winterstein, G., Huang, G.B.: Sentic patterns: dependency-based rules for concept-level sentiment analysis. Knowl.-Based Syst. 69, 45–63 (2014). https://doi.org/10.1016/j.knosys.2014.05.005

    Article  Google Scholar 

  10. Yaqub, U., Chun, S.A., Atluri, V., Vaidya, J.: Analysis of political discourse on Twitter in the context of the 2016 US presidential elections. Gov. Inf. Q. 34(4), 613–626 (2017). https://doi.org/10.1016/j.giq.2017.11.001

    Article  Google Scholar 

  11. Yoo, S., Song, J., Jeong, O.: Social media contents based sentiment analysis and prediction system. Expert Syst. Appl. 105, 102–111 (2018). https://doi.org/10.1016/j.eswa.2018.03.055

    Article  Google Scholar 

  12. Zeroual, I., Lakhouaja, A.: Data science in light of natural language processing: an overview. Procedia Comput. Sci. 127, 82–91 (2018). https://doi.org/10.1016/j.procs.2018.01.101. Proceedings of the first international conference on intelligent computing in data sciences, ICDS 2017

    Article  Google Scholar 

  13. Zhang, F., Fleyeh, H., Wang, X., Lu, M.: Construction site accident analysis using text mining and natural language processing techniques. Autom. Constr. 99, 238–248 (2019). https://doi.org/10.1016/j.autcon.2018.12.016

    Article  Google Scholar 

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Authors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione ed Elettrica e Matematica Applicata - DIEM, Universitá degli Studi di Salerno, Fisciano, SA, Italy

    Danilo Cavaliere & Sabrina Senatore

Authors
  1. Danilo Cavaliere
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  2. Sabrina Senatore
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Corresponding author

Correspondence to Danilo Cavaliere .

Editor information

Editors and Affiliations

  1. International Hellenic University, Thessaloniki, Greece

    Emmanouel Garoufallou

  2. Guglielmo Marconi University, Rome, Italy

    Francesca Fallucchi

  3. Georg Eckert Institute – Leibniz Institute for International Textbook Research, Braunschweig, Germany

    Ernesto William De Luca

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Cavaliere, D., Senatore, S. (2019). Emotional Concept Extraction Through Ontology-Enhanced Classification. In: Garoufallou, E., Fallucchi, F., William De Luca, E. (eds) Metadata and Semantic Research. MTSR 2019. Communications in Computer and Information Science, vol 1057. Springer, Cham. https://doi.org/10.1007/978-3-030-36599-8_5

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  • DOI: https://doi.org/10.1007/978-3-030-36599-8_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36598-1

  • Online ISBN: 978-3-030-36599-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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