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.
CITATION STYLE
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 Multimodal Analytics for Next-Generation Big Data Technologies and Applications (pp. 73–91). Springer International Publishing. https://doi.org/10.1007/978-3-319-97598-6_4
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