We address two understudied areas related to explainability for neural text models. First, class explanations. What features are descriptive across a class, rather than explaining single input instances? Second, the type of features that are used for providing explanations. Does the explanation involve the statistical pattern of word usage or the presence of domain-specific content words? Here, we present a method to extract both class explanations and strategies to differentiate between two types of explanations – domain-specific signals or statistical variations in frequencies of common words. We demonstrate our method using a case study in which we analyse transcripts of political debates in the Swedish Riksdag.

In this paper, we investigate a type of semantic shift that occurs when a sudden event radically changes public opinion on a topic. Looking at Sweden’s decision to apply for NATO membership in 2022, we use word embeddings to study how the associations users on Twitter have regarding NATO evolve. We identify several changes that we successfully validate against real-world events. However, the low engagement of the public with the issue often made it challenging to distinguish true signals from noise. We thus find that domain knowledge and data selection are of prime importance when using word embeddings to study semantic shifts.