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Multimodal Analytics for Next-Generation Big Data Technologies and Applications pp 13–43Cite as

Speaker-Independent Multimodal Sentiment Analysis for Big Data

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Abstract

In this chapter, we propose a contextual multimodal sentiment analysis framework which outperforms the state of the art. This framework has been evaluated against speaker-dependent and speaker-independent problems. We also address the generalizability issue of the proposed method. This chapter also contains a discussion for an important component to be considered for a multimodal information processing system, which is the type of information fusion technique to be applied to combine the multimodal data.

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Notes

  1. 1.

    We have reimplemented the method by Poria et al. [23].

  2. 2.

    RNTN classifies it as neutral. It can be seen here. http://nlp.stanford.edu:8080/sentiment/rntnDemo.html

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Authors and Affiliations

  1. School of Computer Science and Engineering, NTU, Singapore, Singapore

    Erik Cambria & Soujanya Poria

  2. School of Natural Sciences, University of Stirling, Stirling, UK

    Amir Hussain

Authors
  1. Erik Cambria
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  2. Soujanya Poria
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  3. Amir Hussain
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Corresponding author

Correspondence to Erik Cambria .

Editor information

Editors and Affiliations

  1. School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia

    Kah Phooi Seng

  2. School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia

    Li-minn Ang

  3. School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia

    Alan Wee-Chung Liew

  4. The University of Sydney Business School, University of Sydney, Sydney, NSW, Australia

    Junbin Gao

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Cambria, E., Poria, S., Hussain, A. (2019). Speaker-Independent Multimodal Sentiment Analysis for Big Data. In: Seng, K., Ang, Lm., Liew, AC., Gao, J. (eds) Multimodal Analytics for Next-Generation Big Data Technologies and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-97598-6_2

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  • DOI: https://doi.org/10.1007/978-3-319-97598-6_2

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