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Investigating the Role of Emotion-Based Features in Author Gender Classification of Text

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Computational Linguistics and Intelligent Text Processing (CICLing 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8404))

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Abstract

Research has shown that writing styles are influenced by an extensive array of factors that includes text genre and author’s gender. Going beyond the analysis of linguistic features, such as n-grams, stylometric variables and word categories, this paper presents an exploratory study of the role that emotions expressed in writing play to aid discriminating author gender in different text genres. In this work, the gender classification task is seen as a binary classification problem where discriminating features are taken from a vectorial space that includes emotion-based features. Results show that by exploiting the emotional information present in personal journal (diary) texts, up to 80% cross-validation accuracy with support vector machine (SVM) algorithm can be reached. Over 75% cross-validation accuracy is reached when classifying the author gender of blog texts. Our findings show positive implications of emotion-based features on assisting author’s gender classification.

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Authors and Affiliations

  1. School of Computing, University of Eastern Finland, Finland

    Calkin Suero Montero, Myriam Munezero & Tuomo Kakkonen

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  1. Calkin Suero Montero
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  2. Myriam Munezero
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  3. Tuomo Kakkonen
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Editors and Affiliations

  1. Center for Computing Research, National Polytechnic Institute, Av. Juan Dios Bátiz, Col. Nueva Industrial Vallejo, 07738, Mexico D.F, Mexico

    Alexander Gelbukh

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Suero Montero, C., Munezero, M., Kakkonen, T. (2014). Investigating the Role of Emotion-Based Features in Author Gender Classification of Text. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54903-8_9

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  • DOI: https://doi.org/10.1007/978-3-642-54903-8_9

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