A Corpus for Sentence-Level Subjectivity Detection on E nglish News Articles (2024)

Abstract: We develop novel annotation guidelines for sentence-level subjectivity detection, which are not limited to language-specific cues. We use our guidelines to collect NewsSD-ENG, a corpus of 638 objective and 411 subjective sentences extracted from English news articles on controversial topics. Our corpus paves the way for subjectivity detection in English and across other languages without relying on language-specific tools, such as lexicons or machine translation. We evaluate state-of-the-art multilingual transformer-based models on the task in mono-, multi-, and cross-language settings. For this purpose, we re-annotate an existing Italian corpus. We observe that models trained in the multilingual setting achieve the best performance on the task.

## bert-base-multilingual-cased. these results indicate that the multilingual setting is beneficial for certain models. most importantly, no performance loss is observed compared to the monolingual setting, suggesting that the two corpora are coherent in their annotation. lastly, we observe a significant performance loss for m-bert compared to monolingual in cross-lingual. in contrast, m-sbert achieves comparable performance in both corpora. this result is in accordance with the results of reimers and gurevych (2020).