Hybrid feature-based sentiment strength detection for big data applications

Yanghui RAO, Haoran XIE*, Fu Lee WANG, Leonard K. M. POON, Endong ZHU

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsBook Chapter

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 leams 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.
Original languageEnglish
Title of host publicationMultimodal analytics for next-generation big data technologies and applications
EditorsKah Phooi SENG, Li-minn ANG, Alan Wee-Chung LIEW, Junbin GAO
Place of Publication9783319975979
PublisherSpringer International Publishing AG
Pages73-91
Number of pages19
ISBN (Electronic)9783319975986
DOIs
Publication statusPublished - 2019
Externally publishedYes

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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 K. P. SENG, L. ANG, A. W-C. LIEW, & J. GAO (Eds.), Multimodal analytics for next-generation big data technologies and applications (pp. 73-91). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-97598-6_4