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Hybrid Feature-Based Sentiment Strength Detection for Big Data Applications

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Multimodal Analytics for Next-Generation Big Data Technologies and Applications

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Abstract

In this chapter, we focus on the detection of sentiment strength values for a given document. A convolution-based model is proposed to encode semantic and syntactic information as feature vectors, which has the following two characteristics: (1) it incorporates shape and morphological knowledge when generating semantic representations of documents; (2) it divides words according to their part-of-speech (POS) tags and learns POS-level representations for a document by convolving grouped word vectors. Experiments using six human-coded datasets indicate that our model can achieve comparable accuracy with that of previous classification systems and outperform baseline methods over correlation metrics.

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Notes

  1. 1.

    http://sentistrength.wlv.ac.uk/documentation/

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Acknowledgment

The authors are thankful to the reviewers, Huijun Chen, and Xin Li for their constructive comments and valuable feedback and suggestions on this chapter. This research was supported by the National Natural Science Foundation of China (61502545), the Internal Research Grant (RG 92/2017-2018R) of The Education University of Hong Kong, and a grant from Research Grants Council of Hong Kong Special Administrative Region, China (UGC/FDS11/E03/16).

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Authors and Affiliations

  1. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China

    Yanghui Rao & Endong Zhu

  2. Department of Mathematics and Information Technology, The Education University of Hong Kong, New Territories, Hong Kong

    Haoran Xie & Leonard K. M. Poon

  3. School of Science and Technology, The Open University of Hong Kong, Kowloon, Hong Kong

    Fu Lee Wang

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  1. Yanghui Rao
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  2. Haoran Xie
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  3. Fu Lee Wang
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  4. Leonard K. M. Poon
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  5. Endong Zhu
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Correspondence to Haoran Xie .

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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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Rao, Y., Xie, H., Wang, F.L., Poon, L.K.M., Zhu, E. (2019). Hybrid Feature-Based Sentiment Strength Detection for Big Data Applications. 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_4

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  • DOI: https://doi.org/10.1007/978-3-319-97598-6_4

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