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Sentiment Analysis and Ontology Engineering pp 113–139Cite as

Capturing Digest Emotions by Means of Fuzzy Linguistic Aggregation

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 639))

Abstract

Distilling sentiments and moods hidden in the written (natural) language is a challenging issue which attracts research and commercial interests, aimed at studying the users behavior on the Web and evaluating the public attitudes towards brands, social events, political actions. The understanding of the written language is a very complicated task: sentiments and opinions are concealed in the sentences, typically associated to adjectives and verbs; then the intrinsic meaning of some textual expressions is not amenable to rigid linguistic patterns. This work presents a framework for detecting sentiment and emotion from text. It exploits an affective model known as Hourglass of Emotions, a variant of Plutchik’s wheel of emotions. The model defines four affective dimensions, each one with some activation levels, called ‘sentic levels’ that represent an emotional state of mind and can be more or less intense, depending on where they are placed in the corresponding dimension. Our approach draws from the Computational Intelligence area to provide a conceptual setting to sentiment and emotion detection and processing. The novelty is the fuzzy linguistic modeling of the Hourglass of Emotions: dimensions are modeled as fuzzy linguistic variables, whose linguistic terms are the sentic levels (emotions). This linguistic modeling naturally enables the use of fuzzy linguistic aggregation operators (from Computing with Words paradigm), such as LOWA (Linguistic Ordered Weighted Averaging) that inherently accomplishes an aggregation of the emotions in order to get an emotional expression that synthesizes a set of emotions associated with different sentic levels and activation intensities. The whole process for the emotion detection and synthesis is described through its main tasks, from the text parsing up to emotions extraction, returning a predominant emotion, associated with each dimension of the Hourglass of Emotions. An ad-hoc ontology has been designed to integrate lexical information and relations, along with the Hourglass model.

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Notes

  1. 1.

    http://sentiwordnet.isti.cnr.it/.

  2. 2.

    https://jena.apache.org/.

  3. 3.

    http://sourceforge.net/projects/jwordnet/.

  4. 4.

    http://lodlive.it/.

  5. 5.

    http://jena.apache.org/documentation/serving_data/index.html.

  6. 6.

    Java WordNet Similarity Library: http://grid.deis.unical.it/similarity/.

  7. 7.

    Generalizing, the average calculation can be applied on all element whose value is greater than a fixed threshold.

  8. 8.

    http://www.affective-sciences.org/researchmaterial.

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Authors and Affiliations

  1. CORISA (Consorzio di Ricerca Sistemi ad Agenti), Universitá degli Studi di Salerno, 84084, Fisciano, SA, Italy

    C. Brenga & A. Celotto

  2. Dipartimento di Scienze Aziendali - Management & Innovation Systems, Universitá degli Studi di Salerno, 84084, Fisciano, SA, Italy

    V. Loia

  3. Dipartimento di Ingegneria Dell’Informazione ed Elettrica e Matematica Applicata, Universitá degli Studi di Salerno, 84084, Fisciano, SA, Italy

    S. Senatore

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  1. C. Brenga
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  4. S. Senatore
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Correspondence to S. Senatore .

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Editors and Affiliations

  1. Department of Electrical and Computer, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  2. and Technology, National Taiwan University of Scien, Taipei, Taiwan

    Shyi-Ming Chen

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Brenga, C., Celotto, A., Loia, V., Senatore, S. (2016). Capturing Digest Emotions by Means of Fuzzy Linguistic Aggregation. In: Pedrycz, W., Chen, SM. (eds) Sentiment Analysis and Ontology Engineering. Studies in Computational Intelligence, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-30319-2_6

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  • DOI: https://doi.org/10.1007/978-3-319-30319-2_6

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