Emotion Stimulus Detection in German News Headlines

Emotion Stimulus Detection in German News Headlines Bao Minh Doan Dang, Laura Oberl¨ander, and Roman Klinger Institut f¨ur Maschinelle Sprachverarbeitung, University of Stuttgart, Germany st117194@stud.uni-stuttgart.de, {laura.oberlaender, roman.klinger}@ims.uni-stuttgart.de Abstract Emotion stimulus extraction is a fine-grained subtask of emotion analysis that focuses on identifying the description of the cause behind an emotion expression from a text passage (e.g., in the sentence “I am happy that I passed my exam” the phrase “passed my exam” cor- responds to the stimulus.). Previous work mainly focused on Mandarin and English, with no resources or models for German. We fill this research gap by developing a corpus of 2006 German news headlines annotated with emotions and 811 instances with annotations of stimulus phrases. Given that such corpus creation efforts are time-consuming and expen- sive, we additionally work on an approach for projecting the existing English GoodNewsEv- eryone (GNE) corpus to a machine-translated German version. We compare the performance of a conditional random field (CRF) model (trained monolingually on German and cross- lingually via projection) with a multilingual XLM-RoBERTa (XLM-R) model. Our results show that training with the German corpus achieves higher F1 scores than projection. Ex- periments with XLM-R outperform their re- spective CRF counterparts. 1 Introduction Emotions are a complex phenomenon that play a central role in our experiences and daily communi- cations. Understanding them cannot be accounted by any single area of study since they can be rep- resented and expressed in different ways, e.g., via facial expressions, voice, language, or gestures. In natural language processing, most models build on top of one out of three approaches to study and understand emotions, namely basic emotions (Ekman, 1992; Strapparava and Mihalcea, 2007; Aman and Szpakowicz, 2007), the valence-arousal model (Russell, 1980; Buechel and Hahn, 2017) or cognitive appraisal theory (Scherer, 2005; Hof- mann et al., 2020, 2021). Emotion classification in text has received abundant attention in natural language processing research in the past few years. Hence, many studies have been conducted to in- vestigate emotions on social media (Stieglitz and Dang-Xuan, 2013; Brynielsson et al., 2014; Tromp and Pechenizkiy, 2015), in literary and poetry texts (Kim and Klinger, 2019; Haider et al., 2020) or for analysing song lyrics (Mihalcea and Strapparava, 2012; Hijra Ferdinan et al., 2018; Edmonds and Sedoc, 2021). However, previous work mostly fo- cused on assigning emotions to sentences or text passages. These approaches do not allow to iden- tify which event, object, or person caused the emo- tion (which we refer to as the stimulus). Emotion stimulus detection is the subtask of emotion analysis which aims at extracting the stim- ulus of an expressed emotion. For instance, in the following example from FrameNet (Fillmore et al., 2003) “Holmes is happy having the freedom of the house when we are out” one could assume that happiness or joy is the emotion in the text. One could also highlight that the term “happy” indi- cates the emotion, “Holmes” is the experiencer and the phrase “having the freedom of the house when we are …

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