The rise of populism concerns many political scientists and practitioners, yet the detection of its underlying language remains fragmentary. This paper aims to provide a reliable, valid, and scalable approach to measure populist rhetoric. For that purpose, we created an annotated dataset based on parliamentary speeches of the German Bundestag (2013–2021). Following the ideational definition of populism, we label moralizing references to “the virtuous people” or “the corrupt elite” as core dimensions of populist language. To identify, in addition, how the thin ideology of populism is “thickened,” we annotate how populist statements are attached to left-wing or right-wing host ideologies. We then train a transformer-based model (PopBERT) as a multilabel classifier to detect and quantify each dimension. A battery of validation checks reveals that the model has a strong predictive accuracy, provides high qualitative face validity, matches party rankings of expert surveys, and detects out-of-sample text snippets correctly. PopBERT enables dynamic analyses of how German-speaking politicians and parties use populist language as a strategic device. Furthermore, the annotator-level data may also be applied in cross-domain applications or to develop related classifiers.

The proliferation of social networks has caused an increase in the amount of textual content generated by users. The voluminous nature of such content poses a challenge to users, necessitating the development of technological solutions for automatic summarisation. This paper presents a two-stage framework for generating abstractive summaries from a collection of Twitter texts. In the first stage of the framework, event detection is carried out through clustering, followed by event summarisation in the second stage. Our approach involves generating contextualised vector representations of tweets and applying various clustering techniques to the vectors. The quality of the resulting clusters is evaluated, and the best clusters are selected for the summarisation task based on this evaluation. In contrast to previous studies, we experimented with various clustering techniques as a preprocessing step to obtain better event representations. For the summarisation task, we utilised pre-trained models of three state-of-the-art deep neural network architectures and evaluated their performance on abstractive summarisation of the event clusters. Summaries are generated from clusters that contain (a) unranked tweets, (b) all ranked tweets, and (c) the top 10 ranked tweets. Of these three sets of clusters, we obtained the best ROUGE scores from the top 10 ranked tweets. From the summaries generated from the clusters containing the top ten tweets, we obtained ROUGE-1 F score of 48%, ROUGE-2 F score of 37%, ROUGE-L F score of 44%, and ROUGE-SU F score of 33% which suggests that if relevant tweets are at the top of a cluster, and then better summaries are generated.

The use of artificial intelligence (AI) techniques to uncover customer sentiment is not uncommon. However, the integration of sentiment analysis with research in design change prediction remains an untapped potential. This paper presents a method that uses social media sentiment analysis to identify opportunities for design change and the set of product components affected by the change. The method builds on natural language processing to determine change candidates from textual data and uses dependency modeling to reveal direct and indirect change propagation paths arising from the change candidates. The method was applied in a case example where 3665 YouTube comments on a diesel engine were analyzed. Based on the results, two engine components were recommended for design change with six others predicted as likely to be affected through change propagation. The findings suggest that the method can be used to aid decision quality in product planning through a better understanding of the change impact associated with the opportunities identified.

Wines are usually evaluated by wine experts and enthusiasts who give numeric ratings as well as text reviews. While most wine classification studies have been based on conventional statistical models using numeric variables, there has been very limited work on implementing neural network models using wine reviews. In this paper, we apply neural network models (CNN, BiLSTM, and BERT) to extract useful information from wine reviews and classify wines according to different rating classes. Using a large collection of wine reviews from Wine Spectator, the study shows that BERT, a neural network framework recently developed by Google, has the best performance. In the two-class classification (90–100 and 80–89), BERT achieves an accuracy of 89.12%, followed by BiLSTM (88.69%) and CNN (88.02%). In the four-class classification (95–100, 90–94, 85–89, and 80–84), BERT yields an 81.57% accuracy, while the other two produce an 80% accuracy. The neural network models in the paper are independent of domain knowledge and thus can be easily extended to other kinds of text analysis. Expanding the limited work on wine text review classification studies, these models are up-to-date and provide valuable additions to wine data analysis. (JEL Classifications: C45, C88, D83)

This article analyzes questions of literary production and literary value in the Democratic People's Republic of Korea (DPRK) using quantitative methods and data sets covering a significant part of the country's literary and intellectual output over forty years (1977–2016). The first part of the article adopts a thematic approach, using topic modeling and time series, to map the DPRK's literary output and investigate the influence of genre on literary consecration. The second part looks at questions of style and literary quality in the DPRK through a comparative analysis of multiple novels grouped by degree of literary prestige. It introduces a method to evaluate the originality of an author's writing style using a masked language model (BERT) and uses it to assess the role of conformity and originality in the DPRK's literary field. Even though writers consecrated by the state display a high level of stylistic conformity when writing mandated biographies of the ruling family, their independent output ranks higher in originality than nonconsecrated writers.

This study analyzes political polarization among the South Korean elite by examining 17 years’ worth of subcommittee meeting minutes from the South Korean National Assembly's standing committees. Its analysis applies various natural language processing techniques and the bidirectional encoder representations from the transformers model to measure and analyze polarization in the language used during these meetings. Its findings indicate that the degree of political polarization increased and decreased at various times over the study period but has risen sharply since the second half of 2016 and remained high throughout 2020. This result suggests that partisan political gaps between members of the South Korean National Assembly increase substantially.

The previous Emerging Trends article (Church et al., 2021. Natural Language Engineering27(5), 631–645.) introduced deep nets to poets. Poets is an imperfect metaphor, intended as a gesture toward inclusion. The future for deep nets will benefit by reaching out to a broad audience of potential users, including people with little or no programming skills, and little interest in training models. That paper focused on inference, the use of pre-trained models, as is, without fine-tuning. The goal of this paper is to make fine-tuning more accessible to a broader audience. Since fine-tuning is more challenging than inference, the examples in this paper will require modest programming skills, as well as access to a GPU. Fine-tuning starts with a general purpose base (foundation) model and uses a small training set of labeled data to produce a model for a specific downstream application. There are many examples of fine-tuning in natural language processing (question answering (SQuAD) and GLUE benchmark), as well as vision and speech.