@article{GarciaSteinigerReichetal.2006,
  author    = {Garcia, A. L. and Steiniger, J. and Reich, S. C. and Weickert, M. O. and Harsch, I. and Machowetz, A. and Mohlig, M. and Spranger, Joachim and Rudovich, N. N. and Meuser, F. and Doerfer, J. and Katz, N. and Speth, M. and Zunft, Hans-Joachim Franz and Pfeiffer, Andreas F. H. and Koebnick, Corinna},
  title     = {Arabinoxylan fibre consumption improved glucose metabolism, but did not affect serum adipokines in subjects with impaired glucose tolerance},
  series = {Hormone and metabolic research},
  volume    = {38},
  journal   = {Hormone and metabolic research},
  number    = {2},
  publisher = {Thieme},
  address   = {Stuttgart},
  issn      = {0018-5043},
  doi       = {10.1055/s-2006-955089},
  pages     = {761 -- 766},
  year      = {2006},
  abstract  = {The consumption of arabinoxylan, a soluble fibre fraction, has been shown to improve glycemic control in type 2 diabetic subjects. Soluble dietary fibre may modulate gastrointestinal or adipose tissue hormones regulating food intake. The present study investigated the effects of arabinoxylan consumption on serum glucose, insulin, lipids, leptin, adiponectin and resistin in subjects with impaired glucose tolerance. In a randomized, single-blind, controlled, crossover intervention trial, 11 adults consumed white bread rolls as either placebo or supplemented with 15g arabinoxylan for 6 weeks with a 6-week washout period. Fasting serum glucose, insulin, triglycerides, unesterified fatty acids, apolipoprotein A1 and B, adiponectin, resistin and leptin were assessed before and after intervention. Fasting serum glucose, serum triglycerides and apolipoprotein A-1 were significantly lower during arabinoxylan consumption compared to placebo (p = 0.029, p = 0.047; p = 0.029, respectively). No effects of arabinoxylan were observed for insulin, adiponectin, leptin and resistin as well as for apolipoprotein B, and unesterified fatty acids. In conclusion, the consumption of AX in subjects with impaired glucose tolerance improved fasting serum glucose, and triglycerides. However, this beneficial effect was not accompanied by changes in fasting adipokine concentrations.},
  language  = {en}
}
@article{MunnesHarschKnoblochetal.2022,
  author    = {Munnes, Stefan and Harsch, Corinna and Knobloch, Marcel and Vogel, Johannes S. and Hipp, Lena and Schilling, Erik},
  title     = {Examining Sentiment in Complex Texts. A Comparison of Different Computational Approaches},
  series = {Frontiers in Big Data},
  volume    = {5},
  journal   = {Frontiers in Big Data},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {2624-909X},
  doi       = {10.3389/fdata.2022.886362},
  pages     = {16},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}