http://hdl.handle.net/11089/57130 Fri, 03 Apr 2026 23:24:39 GMT 2026-04-03T23:24:39Z http://hdl.handle.net/11089/57154 “Behind Every Successful Woman is a Tribe of Other Successful Women” – A Preliminary Corpus-Assisted Study of Evaluative Adjectives in Women Entrepreneurs’ Blogs Fronczak, Katarzyna Women entrepreneurs’ blogs represent a unique and evolving form of digital discourse, blending professional authority with personal engagement. Within this genre, evaluative language plays a crucial role in shaping credibility, persuasion, and identity. While evaluative adjectives have been widely studied in formal and academic contexts, their opinion-forming function in entrepreneurial communication remains largely unexplored.This study investigates how opinions and evaluations are constructed through the use of evaluative adjectives in women entrepreneurs’ blogs, examining their distribution, rhetorical function, and impact on audience engagement. Using a corpus-assisted methodology, the analysis is conducted on the lexical data extracted from the Women Entrepreneurs Blog Corpus (WEBC), a dataset of 329,896 words from 318 blog posts. The study identifies evaluative adjectives through frequency-based corpus analysis and categorises them into distinct semantic groups, which serve as the foundation for examining how women entrepreneurs use linguistic choices to construct stance, authority, and persuasion in digital business communication.A quantitative analysis of categorised evaluative adjectives reveals that positive-polarity evaluative adjectives are the most frequent, reinforcing optimism and motivation. Adjectives of importance follow, emphasising authority and expertise, while size- and time-related adjectives occur moderately, highlighting growth and progress. Attitude and emotion adjectives appear less frequently, contributing to a confident and engaging tone. Negatively-charged adjectives are rare and often reframed, whereas certainty and likelihood adjectives are the least frequent, reflecting a preference for flexibility over absolutes in entrepreneurial discourse.By situating this analysis within the broader framework of stance and evaluation in specialised discourse, this study provides insights into how women entrepreneurs use language to formulate and express opinions, navigate professional identity, establish credibility, and engage their audiences. The research contributes to discussions on opinion expression in digital business communication, shedding light on the intersection of gender, entrepreneurship, and linguistic strategies in online discourse. Tue, 30 Dec 2025 00:00:00 GMT http://hdl.handle.net/11089/57154 2025-12-30T00:00:00Z http://hdl.handle.net/11089/57155 Over-The-Phone Commuity Interpreting: A Corpus-Based Analysis of Interpreted Interactions in Community Settings Boczarski, Przemyslaw This paper examines the under-researched field of Polish–English telephone interpreting, with particular focus on the interactional environment and methods of connection. It first outlines the emergence of telephone interpreting, considers its advantages and disadvantages, and then presents a summary of findings from a corpus study conducted in 2024, in which 250 Polish–English interpreting interactions were recorded and analysed with reference to the method of connection between parties and the sectoral distribution of topics or issues discussed in those interactions. As OPI is conducted exclusively through the auditory channel, interpreters are deprived of visual cues; consequently, each method of connection presents both benefits and drawbacks, with the three-way conference call emerging as the most effective format for telephonic interactions between Polish speakers who decide to use the services of Polish-English OPI interpreters. Wed, 31 Dec 2025 00:00:00 GMT http://hdl.handle.net/11089/57155 2025-12-31T00:00:00Z http://hdl.handle.net/11089/57152 Detection and Classification of Ideological Texts in the Kazakh Language Using Machine Learning and Transformers Bolatbek, Milana; Mussiraliyeva, Shynar; Baisylbayeva, Kymbat Modern information technologies enable the automatic analysis of textual data to detect extremist and propagandistic content. This paper examines deep learning methods and transformers models for the automatic classification of ideologically charged texts in the Kazakh language. A comparison was conducted between neural network models (CNN, BiLSTM, GRU, Hybrid CNN+BiLSTM) and modern transformers (DistilBERT). The performance evaluation of the models was based on accuracy, recall, precision, and F1-score metrics, as well as error analysis. Experimental results showed that hybrid CNN+BiLSTM demonstrated the highest accuracy (95.11%), outperforming other models. CNN, BiLSTM and GRU also achieved high results (92-93%), making them effective for this task. Among transformers, DistilBERT proved to be the most balanced (85.74%). This study demonstrates that hybrid neural network models (CNN+BiLSTM) are the most effective solution, while DistilBERT performs best among transformer models. The findings can be utilized for developing automatic monitoring and filtering systems for Kazakh-language texts, capable of efficiently identifying ideologically charged content. Tue, 30 Dec 2025 00:00:00 GMT http://hdl.handle.net/11089/57152 2025-12-30T00:00:00Z http://hdl.handle.net/11089/57153 Linguistically Packaging Opinion: A Quantitative Token-Based Analysis of Adjective Structures in English Brosa-Rodríguez, Antoni This study examines the distribution and characteristics of three linguistic structures across three different semantic types of adjectives in English: opinion-based (evaluative), mixed (dimensional), and non-opinion (objective) adjectives. While previous research has explored the perception of subjective adjectives in varied linguistic environments through experimental methods, our research provides quantitative corpus-based evidence from Universal Dependencies English corpora. The analysis focuses on three syntactic structures: modification of a noun (Adj+Noun), predicative construction with nexus (Nexus+Cop+Adj), and predicative construction with noun subject (Noun+Cop+Adj). Results reveal significant patterns: opinion adjectives display greater structural flexibility, appearing with meaningful frequency in both attributive and predicative positions, while dimensional adjectives strongly favor attributive position. The study also identifies consistent positional patterns across all adjective types, with attributive structures typically appearing later in sentences than predicative constructions. Furthermore, register variations were observed, with web language showing distinctive distributional patterns for opinion adjectives compared to more varied texts. Tue, 30 Dec 2025 00:00:00 GMT http://hdl.handle.net/11089/57153 2025-12-30T00:00:00Z