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SenticNet

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Sentiment Analysis in the Bio-Medical Domain

Part of the book series: Socio-Affective Computing ((SAC,volume 7))

Abstract

SenticNet is the knowledge base the sentic computing framework leverages on for concept-level sentiment analysis. This chapter illustrates how such a resource is built. In particular, the chapter thoroughly explains the processes of knowledge acquisition, representation, and reasoning, which contribute to the generation of the semantics and sentics that form SenticNet. This chapter describes the knowledge bases and knowledge sources SenticNet is built upon. Then it describes how the knowledge collected is represented in graph, matrix and vector space. Then it dives into the techniques adopted for generating semantics and sentics, finally discussing how the proposed framework outperforms the state-of-the-art methods.

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Notes

  1. 1.

    http://sentic.net/senticnet-4.0.zip

  2. 2.

    http://sentic.net/api

  3. 3.

    http://freebase.com

  4. 4.

    http://rtw.ml.cmu.edu/rtw

  5. 5.

    http://research.microsoft.com/probase

  6. 6.

    http://sentic.net/affectnet.zip

  7. 7.

    http://sentic.net/affectivespace.zip

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Authors and Affiliations

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

    Ranjan Satapathy & Erik Cambria

  2. Division of Computing Science, University of Stirling, Stirling, UK

    Amir Hussain

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  1. Ranjan Satapathy
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  2. Erik Cambria
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  3. Amir Hussain
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Satapathy, R., Cambria, E., Hussain, A. (2017). SenticNet. In: Sentiment Analysis in the Bio-Medical Domain. Socio-Affective Computing, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-68468-0_3

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