Heterogeneous Recycle Generation for Chinese Grammatical Error Correction

Proceedings of the 28th International Conference on Computational Linguistics, pages 219–224 Barcelona, Spain (Online), December 8-13, 2020 219 Regrexit or not Regrexit: Aspect-based Sentiment Analysis in Polarized Contexts Vorakit Vorakitphan 1, Marco Guerini 2, Elena Cabrio 3, Serena Villata 3 Universit´e Cˆote d’Azur, CNRS, Inria, I3S, France 1,3 Fondazione Bruno Kessler, Trento, Italy 2 vorakit.vorakitphan@inria.fr 1, guerini@fbk.eu 2, {firstname.lastname}@univ-cotedazur.fr 3 Abstract Emotion analysis in polarized contexts represents a challenge for Natural Language Processing modeling. As a step in the aforementioned direction, we present a methodology to extend the task of Aspect-based Sentiment Analysis (ABSA) toward the affect and emotion representation in polarized settings. In particular, we adopt the three-dimensional model of affect based on Va- lence, Arousal, and Dominance (VAD). We then present a Brexit scenario that proves how affect varies toward the same aspect when politically polarized stances are presented. Our approach captures aspect-based polarization from newspapers regarding the Brexit scenario of 1.2m en- tities at sentence-level. We demonstrate how basic constituents of emotions can be mapped to the VAD model, along with their interactions respecting the polarized context in ABSA settings using biased key-concepts (e.g., “stop Brexit” vs. “support Brexit”). Quite intriguingly, the framework achieves to produce coherent aspect evidences of Brexit’s stance from key-concepts, showing that VAD influence the support and opposition aspects. 1 Introduction Aspect-based Sentiment Analysis (ABSA) aims at capturing sentiment (i.e., positive, negative or neutral) expressed toward each aspect (i.e., attribute) of a target entity. The main interest is to capture sentiment nuances about different entities. However, in a context of opinion polarization, different groups of people can form strong convictions of competing opinions on such target entities, resulting in different (often opposite) evaluations of the same aspect. Compare, for example, the differences in the pro- and anti- Brexit discourses concerning the withdrawal of the United Kingdom from the European Union (EU), aligning with contrasting attitudes toward the EU, the immigration and the country’s culture. In fact, while in standard scenarios of sentiment analysis about specific entities and their aspects it is assumed that sentiment is consistent (e.g., a big screen is a desirable characteristics for a TV), this is not the case for polarized contexts. Hence, for example, a “clean Brexit” might be desirable to some, but not to others. Some previous work has showed that sentiment alone (i.e., positive, negative, neutral) is not able to grasp complex emotion phenomena in information diffusion scenarios (Guerini and Staiano, 2015; Berger and Milkman, 2012). Following the suggestion of (Guerini and Staiano, 2015), to analyse in a fine-grained manner our scenario where the same topic can appear in different news outlets that have opposed opinions, we decide to make resort to the model presented in (Russell, 1980; Bradley and Lang, 1994). This work developed a circumplex model, with a three dimensional space of affect along the Va- lence, Arousal, and Dominance coordinates, where all emotions can be mapped in the same dimensional space. In our work, we therefore hypothesize that VAD plays a significant role in …

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