We introduce an argumentation annotation scheme that models basic argumentative structure and additional contextual details across diverse user opinion domains. Drawing from established argumentation modeling approaches and related theory on user opinions, the scheme integrates the concepts of argumentative components, specificity, sentiment and aspects of the user opinion domain. Our freely available dataset includes 1,016 user opinions with 7,266 sentences, spanning products from 19 e-commerce categories, restaurants, hotels, local services, and mobile applications. Utilizing the dataset, we trained three transformer-based models, demonstrating their efficacy in predicting the annotated classes for identifying argumentative statements and contextual details from user opinion documents. Finally, we evaluate a prototypical dashboard that integrates the model inferences to aggregate information and rank exemplary products based on a vast array of user opinions. Early results from an experimental evaluation with eighteen users include positive user perceptions but also highlight challenges when condensing detailed argumentative information to users.

We present an annotation approach for capturing structured components and arguments inlegal case solutions of German students. Based on the appraisal style, which dictates the structured way of persuasive writing in German law, we propose an annotation scheme with annotation guidelines that identify structured writing in legal case solutions. We conducted an annotation study with two annotators and annotated legal case solutions to capture the structures of a persuasive legal text. Based on our dataset, we trained three transformer-based models to show that the annotated components can be successfully predicted, e.g. to provide users with writing assistance for legal texts. We evaluated a writing support system in which our models were integrated in an online experiment with law students and found positive learning success and users’ perceptions. Finally, we present our freely available corpus of 413 law student case studies to support the development of intelligent writing support systems.