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Prediction and Semantic Trained Scales: Examining the Relationship Between Semantic Responses to Depression and Worry and the Corresponding Rating Scales

Kapitel i bok
Författare Oscar N. E. Kjell
Katarina Kjell
Danilo Garcia
Sverker Sikström
Publicerad i Statistical Semantics - Methods and Applications
Förlag Springer
Förlagsort Cham, Switzerland
Publiceringsår 2019
Publicerad vid Psykologiska institutionen
Språk en
Ämnesord Semantic Trained Scales, Laten semantic Algorithm, Depression, Worry
Ämneskategorier Psykiatri, Psykologi

Sammanfattning

This chapter focuses on using the semantic representations, consisting of a number of semantic dimensions, in multiple linear regressions to predict a numerical outcome variable. We examine whether there is a statistically significant relationship between texts and numerical values. Thus, we post the question with what certainty we can predict numerical (or categorical) data from text data? We describe how to use leave-n-out cross-validation to avoid using the same data when both the training data and the evaluation data is in the same dataset. Furthermore, we discuss how to avoid overfitting models. We also describe how trained models can be used to predict numerical values from a new set of text data. Subsequently, research using semantic trained scales in different ways is briefly described to give an idea of how they can be used in different research studies. In particular, we focus on examining the relationship between individuals’ word responses to the semantic questions of depression and worry and their responses to corresponding numerical rating scales using the data from Kjell, Kjell, Garcia and Sikström (2018) as described in Chapter 4.

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