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Making Sentiment Analysis Algorithms Scalable

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Current Trends in Web Engineering (ICWE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11153))

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Abstract

In this paper we introduce a simplified approach to sentiment analysis: a lexicon-driven method based upon only adjectives and adverbs. This method is compared in cross-validation with other known techniques and then compared directly to the gold standard, a sample of human subjects asked to deliver the same class of judgments computed by the method. We prove that the method is similar in accuracy and precision with the other methods. We finally argue that the approach we employ is more valid than others for it is scalable, and exportable to languages other than English.

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Notes

  1. 1.

    OpenNLP package (http://opennlp.sourceforge.net).

  2. 2.

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

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Author information

Authors and Affiliations

  1. University of Verona, Verona, Italy

    Marco Cristani, Matteo Cristani, Anna Pesarin, Claudio Tomazzoli & Margherita Zorzi

Authors
  1. Marco Cristani
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  2. Matteo Cristani
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  3. Anna Pesarin
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  4. Claudio Tomazzoli
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  5. Margherita Zorzi
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Corresponding author

Correspondence to Marco Cristani .

Editor information

Editors and Affiliations

  1. Faculty of Informatics, University of Lugano, Lugano, Switzerland

    Cesare Pautasso

  2. Quercus Software Engineering Group, Universidad de Extremadura, Cáceres, Spain

    Fernando Sánchez-Figueroa

  3. Tampere University of Technology, Tampere, Finland

    Kari Systä

  4. Escuela Politécnica, Universidad de Extremadura, Cáceres, Spain

    Juan Manuel Murillo Rodríguez

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Cristani, M., Cristani, M., Pesarin, A., Tomazzoli, C., Zorzi, M. (2018). Making Sentiment Analysis Algorithms Scalable. In: Pautasso, C., Sánchez-Figueroa, F., Systä, K., Murillo Rodríguez, J. (eds) Current Trends in Web Engineering. ICWE 2018. Lecture Notes in Computer Science(), vol 11153. Springer, Cham. https://doi.org/10.1007/978-3-030-03056-8_12

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  • DOI: https://doi.org/10.1007/978-3-030-03056-8_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03055-1

  • Online ISBN: 978-3-030-03056-8

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