Kokkinakis, DimitriosNermo, MagnusPapadopoulou Skarp, FrantzeskaTienken, SusanneWidholm, AndreasBlåder, Anna2025-12-192025-12-1920251736-6305https://hdl.handle.net/10062/118298This study compares sentiment analysis approaches for Swedish texts using a manually annotated gold-standard dataset. Two methods were examined: i) a multi-label sentiment classifier trained for Swedish, and ii) the Swedish version of VADER, a lexicon-based tool that computes sentiment scores from a vocabulary of polarity-weighted words. The analysis also examined agreement and disagreement between the two methods, with a focus on mixed or context-dependent sentiment. Results indicate that the multi-label classifier aligns more closely with human judgments, especially for medium- or long-text segments with complex or subtle emotional tones. VADER, while prone to errors in idiomatic or nuanced expressions, performs reliably on short, informal utterances, offering computational efficiency and transparency. A hybrid approach combining classifier predictions with lexicon-based scores was investigated to leverage their complementary strengths. Findings underscore the value of rigorous evaluation against human annotations and highlight strategies to improve sentiment analysis in under-resourced languages such as Swedish.enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/sentiment analysismulti-label classifiermulti-class modellexicon-based method (VADER/svVADER)Swedish datasetArticle