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Concept-Level Sentiment Analysis with SenticNet

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A Practical Guide to Sentiment Analysis

Part of the book series: Socio-Affective Computing ((SAC,volume 5))

Abstract

SenticNet is a publicly available resource for opinion mining that exploits AI, linguistics, and psychology to infer the polarity associated with commonsense concepts and encode this in a semantic-aware representation. In particular, SenticNet uses dimensionality reduction to calculate the affective valence of multi-word expressions and, hence, represent it in a machine-accessible and machine-processable format. This chapter presents an overview of the most recent sentic computing tools and techniques, with particular focus on applications in the context of big social data analysis.

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Notes

  1. 1.

    http://sentic.net/api

  2. 2.

    http://sentic.net/downloads

  3. 3.

    http://sentic.net/demo

  4. 4.

    http://business.sentic.net

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Author information

Authors and Affiliations

  1. DITEN, University of Genoa, Via Opera Pia 11A, Genova, I-16145, Italy

    Federica Bisio, Claudia Meda, Paolo Gastaldo & Rodolfo Zunino

  2. School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore, Singapore

    Erik Cambria

Authors
  1. Federica Bisio
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  2. Claudia Meda
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  3. Paolo Gastaldo
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  4. Rodolfo Zunino
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  5. Erik Cambria
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Corresponding author

Correspondence to Federica Bisio .

Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Erik Cambria

  2. Computer Science and Engineering Department, Jadavpur University, Kolkata, India

    Dipankar Das

  3. Computer Science and Engineering Department, Jadavpur University, Kolkata, India

    Sivaji Bandyopadhyay

  4. Fraunhofer IDM@NTU, Nanyang Technological University, Singapore, Singapore

    Antonio Feraco

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Bisio, F., Meda, C., Gastaldo, P., Zunino, R., Cambria, E. (2017). Concept-Level Sentiment Analysis with SenticNet. In: Cambria, E., Das, D., Bandyopadhyay, S., Feraco, A. (eds) A Practical Guide to Sentiment Analysis. Socio-Affective Computing, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-55394-8_9

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