Abstract
The theme of this paper has its origins in linguistic theory, namely that the meaning of multi-word concepts such as “ice cream”, “acid rain” or “city wall” cannot be understood simply from the meaning of their individual parts. The semantically richer nature of multi-word concepts that ensues from their specificity is the subject of analysis of this paper. This chapter contains three main sections. Section 1.1 discusses the ubiquitous nature of opinionated digital text, and the potential application domains for the analysis of sentiment expressed in such texts. Section 1.2 provides an overview of existing sentiment analysis approaches and introduces the SenticNet sentiment lexicon. Section 1.3 presents the research question this paper aims to answer, namely whether multi-word concepts outperform single-word concepts in a sentiment classification task by virtue of their semantic richness. The section outlines the reasons why SenticNet was deemed an ideal platform for investigating the research question.
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Biagioni, R. (2016). Introduction. In: The SenticNet Sentiment Lexicon: Exploring Semantic Richness in Multi-Word Concepts. SpringerBriefs in Cognitive Computation, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-38971-4_1
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DOI: https://doi.org/10.1007/978-3-319-38971-4_1
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