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Semantic Similarity Scales: Using Semantic Similarity Scale to Measure Depression and Worry

Chapter in book
Authors Oscar N. E. Kjell
Katarina Kjell
Danilo Garcia
Sverker Sikström
Published in Statistical Semantics - Methods and Applications
Publisher Springer
Place of publication Cham, Switzerland
Publication year 2019
Published at Department of Psychology
Language en
Keywords Semantic Similarity, Latent Semantic Algorithm, Depression, Worry
Subject categories Psychiatry, Psychology

Abstract

This chapter describes how semantic representations based on Latent Semantic Analysis (LSA; Landauer & Dumais, 1997) may be used to measure the semantic similarity between two words, sets of words or texts. Whereas Nielsen and Hansen describe how to create semantic representations in chapters 2 and 3; this chapter focuses on describing how these may be used in research to estimate how similar words/texts are in meaning as well as testing whether two sets of words statistically differ. This approach may be used to detect in and/or between group differences in an experimental design. First, we describe how a single word’s semantic representation may be added together to describe the meaning of several words or an entire text. Second, we discuss how to measure semantic similarity using cosine of the angle of the words’ position in the semantic space. Third, we describe how this procedure of text quantification makes it possible for researchers to use common statistical tests (e.g., semantic t-test) for investigating, for example, differences between freely generated narratives among. Lastly, we carry out a research study building on studies by Kjell, Kjell, Garcia and Sikström (2018) that demonstrated that semantic similarity scales may be used to measure, differentiate and describe psychological constructs, including depression and worry, independent from traditional numerical rating scales.

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