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. Copyright © 2019 Springer Nature Switzerland AG.
|Title of host publication||Multimodal analytics for next-generation big data technologies and applications|
|Editors||Kah Phooi SENG, Li-minn ANG, Alan Wee-Chung LIEW, Junbin GAO|
|Place of Publication||Cham|
|Publication status||Published - 2019|