Theory-driven text analysis has made extensive use of psychological concept dictionaries, leading to a wide range of important results. These dictionaries have generally been applied through word count methods which have proven to be both simple and effective. In this paper, we introduce Distributed Dictionary Representations (DDR), a method that applies psychological dictionaries using semantic similarity rather than word counts. This allows for the measurement of the similarity between dictionaries and spans of text ranging from complete documents to individual words. We show how DDR enables dictionary authors to place greater emphasis on construct validity without sacrificing linguistic coverage. We further demonstrate the benefits of DDR on two real-world tasks and finally conduct an extensive study of the interaction between dictionary size and task performance. These studies allow us to examine how DDR and word count methods complement one another as tools for applying concept dictionaries and where each is best applied. Finally, we provide references to tools and resources to make this method both available and accessible to a broad psychological audience. Keywords Methodological innovation · Text analysis · Semantic representation · Dictionary-based text analysisElectronic supplementary material The online version of this article

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Recent years have seen rapid developments in automated text analysis methods focused on measuring psychological and demographic properties. While this development has mainly been driven by computer scientists and computational linguists, such methods can be of great value for social scientists in general, and for psychologists in particular. In this paper, we review some of the most popular approaches to automated text analysis from the perspective of social scientists, and give examples of their applications in different theoretical domains. After describing some of the pros and cons of these methods, we speculate about future methodological developments, and how they might change social sciences. We conclude that, despite the fact that current methods have many disadvantages and pitfalls compared to more traditional methods of data collection, the constant increase of computational power and the wide availability of textual data will inevitably make automated text analysis a common tool for psychologists.

When do people see self-control as a moral issue? We hypothesize that the group-focused "binding" moral values of Loyalty/betrayal, Authority/subversion, and Purity/degradation play a particularly important role in this moralization process. Nine studies provide support for this prediction. First, moralization of self-control goals (e.g., losing weight, saving money) is more strongly associated with endorsing binding moral values than with endorsing individualizing moral values (Care/harm, Fairness/cheating). Second, binding moral values mediate the effect of other group-focused predictors of self-control moralization, including conservatism, religiosity, and collectivism. Third, guiding participants to consider morality as centrally about binding moral values increases moralization of self-control more than guiding participants to consider morality as centrally about individualizing moral values. Fourth, we replicate our core finding that moralization of self-control is associated with binding moral values across studies differing in measures and design-whether we measure the relationship between moral and self-control language across time, the perceived moral relevance of self-control behaviors, or the moral condemnation of self-control failures. Taken together, our findings suggest that self-control moralization is primarily group-oriented and is sensitive to group-oriented cues. (PsycINFO Database Record