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EmoSenticSpace: Dense Concept-Based Affective Features with Common-Sense Knowledge

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Multimodal Sentiment Analysis

Part of the book series: Socio-Affective Computing ((SAC,volume 8))

Abstract

This chapter proposes EmoSenticSpace, a new framework for affective common-sense reasoning that extends WordNet-Affect and SenticNet by providing both emotion labels and polarity scores for a large set of natural language concepts. The framework is built by means of fuzzy c-means clustering and support-vector-machine classification, and takes into account a number of similarity measures, including point-wise mutual information and emotional affinity. EmoSenticSpace was tested on three emotion-related natural language processing tasks, namely sentiment analysis, emotion recognition, and personality detection. In all cases, the proposed framework outperforms the state-of-the-art. In particular, the direct evaluation of EmoSenticSpace against psychological features provided in the benchmark ISEAR dataset shows a 92.15

Part of this chapter is reprinted from Knowledge-Based Systems, 69, 108-123, Poria, Gelbukh, Cambria, Hussain, Huang, “EmoSenticSpace: A novel framework for affective commonsense reasoning” 2014, with permission from Elsevier.

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Author information

Authors and Affiliations

  1. Artificial Intelligence Initiative, ASTAR, Singapore, Singapore

    Soujanya Poria

  2. Computing Science and Mathematics, University of Stirling, Stirling, UK

    Amir Hussain

  3. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Erik Cambria

Authors
  1. Soujanya Poria
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  2. Amir Hussain
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  3. Erik Cambria
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Poria, S., Hussain, A., Cambria, E. (2018). EmoSenticSpace: Dense Concept-Based Affective Features with Common-Sense Knowledge. In: Multimodal Sentiment Analysis. Socio-Affective Computing, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-95020-4_5

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