Regrexit or not Regrexit: Aspect-based Sentiment Analysis in Polarized Contexts (2020)

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 Valence, 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 entities 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.

## 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 @xcite . following the suggestion of @xcite , 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 @xcite . this work developed a circumplex model, with a three dimensional space of affect along the valence, 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 the interaction with linguistic elements in absa settings where polarized opinions are at play. more specifically, we answer the following research questions: how basic constituents of emotions, such as vad, interact with aspect-based polarization? how polarized settings differ from standard sa scenarios? how aspects of brexit and related key-concepts are perceived by opposite parties? to answer these research questions, we propose a comprehensive framework for studying the interaction of absa with opinion polarization in newspapers and social media. we first trained an rnn that detects the emotions and their intensities at sentence-level, and then we map emotion intensities into the vad circumplex model. later, we build a framework to assess whether and how vad are connected to polarized contexts, by computing the vad scores of a set of key-concepts that can be found on newspapers with opposite views. these key-concepts (e.g., "stop immigration") are built from a set of aspects ("immigration" in our example) combined with relevant verbs or adjectives that represent a clear polarized opinion toward the aspect (e.g., "stop"). to experiment with the proposed approach, we focus on the brexit scenario, whose unique circumstances have boosted opinion polarization because of the extremely rapid and volatile changes in the political panorama as the results of the eu referendum were announced (political leaders were quick to say that the outcome had "revealed a divided britain") 1 . while an analysis of this situation from the viewpoint of political sciences is not the focus of our work, the brexit scenario as discussed on newspapers and on social media provides us with the required elements to carry out our study, because of the opinion divisions formed around one or more political positions or issues. moreover, to be meaningfully polarizing, issues need to be important (or "salient") to a large section of the public, not just a minority of people with strongly held views, and in the brexit scenario this is actually the case. in our experimental setting, we select two british newspapers known to be polarized, i.e., either for or against brexit. results show that vad are not absolute, but relative to the newspaper's viewpoint on the key-concept. our approach highlights that using the proposed key-concepts gives us fine-grained details about vad elements that strongly interact with the polarized context. we show that standard sa approaches can be deceptive in such polarized setting (considering only the word "brexit" on both newspapers, the valence is almost identical), while our absa approach shows a clear-cut polarization. ## emotions analysis as the first step of the proposed framework, we focus on the task of detecting emotions and their intensities at sentence-level from the news context. we compare two standard emotion recognition approaches, i.e., a lexicon-based model called depechemood++ @xcite , and an rnn based approach that casts the task of multi-label absa as a regression problem. both depechemood++ and the rnn model were built and trained on the same set of news extracted from the rappler.com website. rappler.com is an online news publisher, where each article is associated to a mood meter. such mood meter allows each reader to vote the emotions evoked by the article after reading. we implemented a web crawler and harvested a total of 67,828 articles from january 2017 until april 2019 along with the 8 possible emotions voted by readers (i.e., happy, inspired, amused, afraid, angry, annoyed, don't care, sad). as we aim at detecting emotions at sentence-level, we pick all headlines of such articles and the associated emotions as our training set. for the rnn based approach, we use a deep contextualized word representation as our features, namely elmo @xcite , with a deep bidirectional language model (bilstm). we use bilstm of 128 dimensional layers with a rectified linear unit (relu) activation function @xcite . then, we apply mean squared error (mse) as a loss function with a standard adam optimizer. at the last layer, we use sigmoid function to gate our multi-label output neurons as the intensity of each emotion. to validate our model, we perform cross-dataset testing on the "affective text" dataset from semeval2007-t14 (strapparava and mihalcea, 2007) using pearson's correlation (r) to evaluate the performances of our system on each individual emotion. in given that the elmo embeddings with bilstm model outperformed the other models, we selected the output of this model as input for the next step (i.e., to map the predicted emotions to the vad model). ## vad analysis the vad model of affects @xcite has been extensively used, and relies on three dimensions: valence, that can be positive/negative (e.g., fear has a negative valence, joy has a positive valence) and corresponds to the standard dimension of sentiment analysis; arousal, that can be low or high and ranges from "calm" to "excitement" (e.g., sadness has low arousal while anger has high arousal, even if they have the same sentiment); finally, dominance, that can be low or high as well, and ranges from "controlled" to "in control" (e.g. fear refers to low dominance, inspired to high dominance). to map the emotions expressed in a sentence to the vad model, we followed the approach of @xcite . we use the vad scores of emotion labels provided in @xcite which serves as our gold standard to obtain vad scores from emotion intensities. in particular, here below we report the procedure for valence computation (arousal and dominance computation are akin): given a sentence s, to obtain its valence s v we multiply the intensity of each emotion i(e i ) -computed using the approach described in section 3 -with the corresponding valence score v (e i ) present in @xcite . then, we sum the n emotional dimensions to obtain the final valence score, as expressed in equation 1: after the conversion, vad values are in absolute form (arbitrary range). since newspapers could have different "affective styles" -that are prior to their stance on a specific topic -we apply standardization individually to each vad dimension for each newspaper to make results better comparable across newspapers. ## conclusions and future work in this work, we presented a methodology to extend the task of aspect-based sentiment analysis so that to include affect and emotions representation in polarized settings. in particular, we adopted the three-dimensional model of affect based on valence, arousal, and dominance. we tested the proposed framework on the brexit scenario, showing how affect dimensions (vad) vary on the same aspect when politically polarized stances are presented. our approach was able to capture stereotypical aspect-based polarization from newspapers regarding the brexit scenario using biased key-concepts (e.g., "stop brexit" vs. "support brexit"). as for future work, we plan to investigate the correlation of our results with virality indexes on social media (e.g., number of likes and retweets, number of replied messages). we also plan to have a comparative analysis with the textual material produced by the large audience that is supposed to share the same stance/polarization as the newspaper considering the comments to the articles.