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Can we rely on computational methods to accurately analyze complex texts? To answer this question, we compared different dictionary and scaling methods used in predicting the sentiment of German literature reviews to the "gold standard " of human-coded sentiments. Literature reviews constitute a challenging text corpus for computational analysis as they not only contain different text levels-for example, a summary of the work and the reviewer's appraisal-but are also characterized by subtle and ambiguous language elements. To take the nuanced sentiments of literature reviews into account, we worked with a metric rather than a dichotomous scale for sentiment analysis. The results of our analyses show that the predicted sentiments of prefabricated dictionaries, which are computationally efficient and require minimal adaption, have a low to medium correlation with the human-coded sentiments (r between 0.32 and 0.39). The accuracy of self-created dictionaries using word embeddings (both pre-trained and self-trained) was considerably lower (r between 0.10 and 0.28). Given the high coding intensity and contingency on seed selection as well as the degree of data pre-processing of word embeddings that we found with our data, we would not recommend them for complex texts without further adaptation. While fully automated approaches appear not to work in accurately predicting text sentiments with complex texts such as ours, we found relatively high correlations with a semiautomated approach (r of around 0.6)-which, however, requires intensive human coding efforts for the training dataset. In addition to illustrating the benefits and limits of computational approaches in analyzing complex text corpora and the potential of metric rather than binary scales of text sentiment, we also provide a practical guide for researchers to select an appropriate method and degree of pre-processing when working with complex texts.
This study examines how public policies affect parents' preferences for a more egalitarian division of paid and unpaid work. Based on the assumption that individuals develop their preferences within a specific policy context, we examine how changes in three policies affect mothers' and fathers' work-family preferences: the availability of high-quality, affordable childcare; the right to return to a full-time job after having reduced hours to part-time and an increase in the number of 'partner months' in parental leave schemes. Analysing a unique probability sample of parents with young children in Germany from 2015 (N = 1756), we find that fathers would want to work slightly fewer hours if they had the right to return to a full-time position after working part-time, and mothers would want to work slightly more hours if childcare opportunities were improved. Full-time working parents, moreover, are found to prefer fewer hours independent of the policy setting, while non-employed parents would like to work at least some hours. Last but not least, our analyses show that increasing the number of partner months in the parental leave scheme considerably increases fathers' preferences for longer and mothers' preferences for shorter leave. Increasing the number of partner months in parental schemes hence has the greatest potential to increase gender equality.