TY  - JOUR
A1  - Munnes, Stefan
A1  - Harsch, Corinna
A1  - Knobloch, Marcel
A1  - Vogel, Johannes S.
A1  - Hipp, Lena
A1  - Schilling, Erik
T1  - Examining Sentiment in Complex Texts. A Comparison of Different Computational Approaches
JF  - Frontiers in Big Data
N2  - 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.
KW  - sentiment analysis
KW  - German literature
KW  - dictionary
KW  - word embeddings
KW  - automated text analysis
KW  - computer-assisted text analysis
KW  - scaling method
Y1  - 2022
U6  - https://doi.org/10.3389/fdata.2022.886362
SN  - 2624-909X
VL  - 5
PB  - Frontiers Media
CY  - Lausanne
ER  - 
TY  - JOUR
A1  - Smith, Garrett
A1  - Vasishth, Shravan
T1  - A principled approach to feature selection in models of sentence processing
JF  - Cognitive science : a multidisciplinary journal of anthropology, artificial intelligence, education, linguistics, neuroscience, philosophy, psychology ; journal of the Cognitive Science Society
N2  - Among theories of human language comprehension, cue-based memory retrieval has proven to be a useful framework for understanding when and how processing difficulty arises in the resolution of long-distance dependencies. Most previous work in this area has assumed that very general retrieval cues like [+subject] or [+singular] do the work of identifying (and sometimes misidentifying) a retrieval target in order to establish a dependency between words. However, recent work suggests that general, handpicked retrieval cues like these may not be enough to explain illusions of plausibility (Cunnings & Sturt, 2018), which can arise in sentences like The letter next to the porcelain plate shattered. Capturing such retrieval interference effects requires lexically specific features and retrieval cues, but handpicking the features is hard to do in a principled way and greatly increases modeler degrees of freedom. To remedy this, we use well-established word embedding methods for creating distributed lexical feature representations that encode information relevant for retrieval using distributed retrieval cue vectors. We show that the similarity between the feature and cue vectors (a measure of plausibility) predicts total reading times in Cunnings and Sturt's eye-tracking data. The features can easily be plugged into existing parsing models (including cue-based retrieval and self-organized parsing), putting very different models on more equal footing and facilitating future quantitative comparisons.
KW  - Cue‐based retrieval
KW  - plausibility
KW  - word embeddings
KW  - linguistic
KW  - features
Y1  - 2020
U6  - https://doi.org/10.1111/cogs.12918
SN  - 0364-0213
SN  - 1551-6709
VL  - 44
IS  - 12
PB  - Wiley
CY  - Hoboken
ER  -