This article sheds light on the significant yet nuanced roles of shame and guilt in influencing moral behaviour, a phenomenon that became particularly prominent during the COVID-19 pandemic with the community’s heightened desire to be seen as moral. These emotions are central to human interactions, and the question of how they are conveyed linguistically is a vast and important one. Our study contributes to this area by analysing the discourses around shame and guilt in English and Japanese online forums, focusing on the terms shame, guilt, haji (‘shame’) and zaiakukan (‘guilt’). We utilise a mix of corpus-based methods and natural language processing tools, including word embeddings, to examine the contexts of these emotion terms and identify semantically similar expressions. Our findings indicate both overlaps and distinct differences in the semantic landscapes of shame and guilt within and across the two languages, highlighting nuanced ways in which these emotions are expressed and distinguished. This investigation provides insights into the complex dynamics between emotion words and the internal states they denote, suggesting avenues for further research in this linguistically rich area.

How do politicians use moral appeals in their rhetoric? Previous research suggests that morality plays an important role in elite communication and that the endorsement of specific values varies systematically across the ideological spectrum. We argue that this view is incomplete since it only focuses on whether certain values are endorsed and not how they are contextualized by politicians. Using a novel sentence embedding approach, we show that although liberal and conservative politicians use the same moral terms, they attach diverging meanings to these values. Accordingly, the politics of morality is not about the promotion of specific moral values per se but, rather, a competition over their respective meaning. Our results highlight that simple dictionary-based methods to measure moral rhetoric may be insufficient since they fail to account for the semantic contexts in which words are used and, therefore, risk overlooking important features of political communication and party competition.

Previous research on emotional language relied heavily on off-the-shelf sentiment dictionaries that focus on negative and positive tone. These dictionaries are often tailored to nonpolitical domains and use bag-of-words approaches which come with a series of disadvantages. This paper creates, validates, and compares the performance of (1) a novel emotional dictionary specifically for political text, (2) locally trained word embedding models combined with simple neural network classifiers, and (3) transformer-based models which overcome limitations of the dictionary approach. All tools can measure emotional appeals associated with eight discrete emotions. The different approaches are validated on different sets of crowd-coded sentences. Encouragingly, the results highlight the strengths of novel transformer-based models, which come with easily available pretrained language models. Furthermore, all customized approaches outperform widely used off-the-shelf dictionaries in measuring emotional language in German political discourse.

A central theme in the study of international relations is that anarchy requires states to set aside moral concerns to attain security, rendering IR an autonomous sphere devoid of ethical considerations. Evolutionary and moral psychology, however, suggest that morality emerged to promote human success under such conditions. It is not despite anarchy but because of anarchy that humans have an ethical sense. Our argument has three empirical implications. First, it is almost impossible to talk about threat and harm without invoking morality. Second, state leaders and the public will use moral judgments as a basis, indeed the most important factor, for assessing international threat, just as research shows they do at the interpersonal level. Third, foreign policy driven by a conception of international relations as an amoral sphere will be quite rare. Word embeddings applied to large political and nonpolitical corpora, a survey experiment in Russia, and an in-depth analysis of Hitler's foreign policy thought suggest that individuals both condemn aggressive behavior by others and screen for threats on the basis of morality. The findings erode notions of IR as an autonomous sphere and upset traditional materialist–ideational dichotomies.

As a ubiquitous method in natural language processing, word embeddings are extensively employed to map semantic properties of words into a dense vector representation. They capture semantic and syntactic relations among words, but the vectors corresponding to the words are only meaningful relative to each other. Neither the vector nor its dimensions have any absolute, interpretable meaning. We introduce an additive modification to the objective function of the embedding learning algorithm that encourages the embedding vectors of words that are semantically related to a predefined concept to take larger values along a specified dimension, while leaving the original semantic learning mechanism mostly unaffected. In other words, we align words that are already determined to be related, along predefined concepts. Therefore, we impart interpretability to the word embedding by assigning meaning to its vector dimensions. The predefined concepts are derived from an external lexical resource, which in this paper is chosen as Roget’s Thesaurus. We observe that alignment along the chosen concepts is not limited to words in the thesaurus and extends to other related words as well. We quantify the extent of interpretability and assignment of meaning from our experimental results. Manual human evaluation results have also been presented to further verify that the proposed method increases interpretability. We also demonstrate the preservation of semantic coherence of the resulting vector space using word-analogy/word-similarity tests and a downstream task. These tests show that the interpretability-imparted word embeddings that are obtained by the proposed framework do not sacrifice performances in common benchmark tests.

Anomaly detection can be seen as an unsupervised learning task in which a predictive model created on historical data is used to detect outlying instances in new data. This work addresses possibly promising but relatively uncommon application of anomaly detection to text data. Two English-language and one Polish-language Internet discussion forums devoted to psychoactive substances received from home-grown plants, such as hashish or marijuana, serve as text sources that are both realistic and possibly interesting on their own, due to potential associations with drug-related crime. The utility of two different vector text representations is examined: the simple bag of words representation and a more refined Global Vectors (GloVe) representation, which is an example of the increasingly popular word embedding approach. They are both combined with two unsupervised anomaly detection methods, based on one-class support vector machines (SVM) and based on dissimilarity to k-medoids clusters. The GloVe representation is found definitely more useful for anomaly detection, permitting better detection quality and ameliorating the curse of dimensionality issues with text clustering. The cluster dissimilarity approach combined with this representation outperforms one-class SVM with respect to detection quality and appears a more promising approach to anomaly detection in text data.

We study regional similarities and differences in language use on an anonymous mobile chat application in the German-speaking area. We use a neural network on 2.3 million online conversations to automatically learn representations of words and cities. These linguistic-use-based representations capture regional distinctions in a high-dimensional vector space that can be clustered and visualized to discover patterns in the data. We find that the resulting regional patterns are closely linked to the traditional division of German dialects, even though most of the conversations are written in standard German. The resulting maps correspond to traditional dialect divisions and language-external spatial structures, with a few notable exceptions that can be explained through external factors.

Our method also facilitates two qualitative analyses, allowing us to discover geographically-pertinent words for various regional levels, as well as creating regional group-specific style profiles based on various linguistic resources. The results of our study strongly suggest the existence of region-specific patterns of language use (“digital regiolects”) representing distinctive strategies of linguistic stylization in relation to linguistic resources and topics. As a methodological contribution, we show how linguistic theory can drive the application and direction of neural network-based representation learning, and how their judicious application provides the basis for qualitative analysis of large-scale data collections.

Most compositional distributional semantic models represent sentence meaning with a single vector. In this paper, we propose a structured distributional model (SDM) that combines word embeddings with formal semantics and is based on the assumption that sentences represent events and situations. The semantic representation of a sentence is a formal structure derived from discourse representation theory and containing distributional vectors. This structure is dynamically and incrementally built by integrating knowledge about events and their typical participants, as they are activated by lexical items. Event knowledge is modelled as a graph extracted from parsed corpora and encoding roles and relationships between participants that are represented as distributional vectors. SDM is grounded on extensive psycholinguistic research showing that generalized knowledge about events stored in semantic memory plays a key role in sentence comprehension.We evaluate SDMon two recently introduced compositionality data sets, and our results show that combining a simple compositionalmodel with event knowledge constantly improves performances, even with dif ferent types of word embeddings.

Word embeddings, the coefficients from neural network models predicting the use of words in context, have now become inescapable in applications involving natural language processing. Despite a few studies in political science, the potential of this methodology for the analysis of political texts has yet to be fully uncovered. This paper introduces models of word embeddings augmented with political metadata and trained on large-scale parliamentary corpora from Britain, Canada, and the United States. We fit these models with indicator variables of the party affiliation of members of parliament, which we refer to as party embeddings. We illustrate how these embeddings can be used to produce scaling estimates of ideological placement and other quantities of interest for political research. To validate the methodology, we assess our results against indicators from the Comparative Manifestos Project, surveys of experts, and measures based on roll-call votes. Our findings suggest that party embeddings are successful at capturing latent concepts such as ideology, and the approach provides researchers with an integrated framework for studying political language.

In this paper, we address query-based summarization of discussion threads. New users can profit from the information shared in the forum, Please check if the inserted city and country names in the affiliations are correct. if they can find back the previously posted information. However, discussion threads on a single topic can easily comprise dozens or hundreds of individual posts. Our aim is to summarize forum threads given real web search queries. We created a data set with search queries from a discussion forum’s search engine log and the discussion threads that were clicked by the user who entered the query. For 120 thread–query combinations, a reference summary was made by five different human raters. We compared two methods for automatic summarization of the threads: a query-independent method based on post features, and Maximum Marginal Relevance (MMR), a method that takes the query into account. We also compared four different word embeddings representations as alternative for standard word vectors in extractive summarization. We find (1) that the agreement between human summarizers does not improve when a query is provided that: (2) the query-independent post features as well as a centroid-based baseline outperform MMR by a large margin; (3) combining the post features with query similarity gives a small improvement over the use of post features alone; and (4) for the word embeddings, a match in domain appears to be more important than corpus size and dimensionality. However, the differences between the models were not reflected by differences in quality of the summaries created with help of these models. We conclude that query-based summarization with web queries is challenging because the queries are short, and a click on a result is not a direct indicator for the relevance of the result.