Towards a Multi-Entity Aspect-Based Sentiment Analysis for Characterizing Directed Social Regard in Online Messaging (2022)

Abstract: Online messaging is dynamic, influential, and highly contextual, and a single post may contain contrasting sentiments towards multiple entities, such as dehumanizing one actor while empathizing with another in the same message. These complexities are important to capture for understanding the systematic abuse voiced within an online community, or for determining whether individuals are advocating for abuse, opposing abuse, or simply reporting abuse. In this work, we describe a formulation of directed social regard (DSR) as a problem of multi-entity aspect-based sentiment analysis (ME-ABSA), which models the degree of intensity of multiple sentiments that are associated with entities described by a text document. Our DSR schema is informed by Bandura’s psychosocial theory of moral disengagement and by recent work in ABSA. We present a dataset of over 2,900 posts and sentences, comprising over 24,000 entities annotated for DSR over nine psychosocial dimensions by three annotators. We present a novel transformer-based ME-ABSA model for DSR, achieving favorable preliminary results on this dataset.

## introduction the social media landscape is a complex, dynamic information environment where actors express advocacy, opposition, empathy, dehumanization, and various moralistic signals, with the intent-or sometimes the side-effect-of influencing others. a single message may also express multiple sentiments in one sentence, e.g., opposing one political candidate and endorsing another, or blaming one party for harming another, or dehumanizing one party and empathizing with another. the complexity of multiple sentiments-which may comprise multiple strategies of influence-in a single message means that classifying an entire tweet's sentiment @xcite , or even quantifying it @xcite , along a single dimension, is both at too high a granularity (i.e., we want to assess the author's perspective on multiple topics) and at too few dimensions (i.e., we want to assess the author's perspective along multiple dimensions). aspect-based sentiment analysis (absa) @xcite , allowing multiple dimensions of sentiment on a message, gets us part-way to a solution. multi-entity absa (me-absa) (tao and fang, 2020) gets us further in this direction by classifying along multiple dimensions across entities, but these models are frequently expressed as classification problems (e.g., positive, neutral, and negative predictions), and we desire a finer-grained numerical approach. in the present work, we present a novel multientity transformer-based absa regression implementation of directed social regard (dsr), the prediction of social attitudes directed toward various actors and topics mentioned in the text. social attitudes are modelled along nine continously-valued sentiment aspects: advocate, oppose, dehumanization, empathy, violent, condemn, justified, responsible, and harmed. masked language modelling methods are utilized to support sets of aspects associated with each unique entity type. in the present work, dsr is computed for each character (i.e., human individual, human group, or ideology) in a message and each event that harms characters within a message. also in the present work, the dsr dimensions are informed in part by bandura's psychosocial theory of moral disengagement @xcite , which we describe below. to implement and validate our approach, three labelers rated nine dimensions of social regard for each character and event in a dataset of englishlanguage social media posts sourced from curated twitter datasets. to model dsr, we desitned a transformer-based regression architecture designed specifically for fine-grained sentiment analysis of multiple entities. we next describe the psychosocial theory of moral disengagement. we then describe our approach and empirical results, closing with a discussion of limitations and future work. ## approach we describe our knowlege graph and attibute schema, sources of textual data, annotation process, and our architecture for representing and scoring attributes of social regard. ## experiment we evaluated the dsr/absa architecture on the above dataset with the above dsr schema. we used human-labeled characters and events as inputs for this experiment in order to focus the evaluation on the dsr rather than the span extraction, but we report that on a 90/10 train/test split, the entity extractor scored f1 scores of 0.95 and 0.73 for extracting characters and events, allowing determiner mismatch, e.g., an event "the airstrikes" is allowed to match to "airstrikes." we use the pre-trained, case-sensitive bertbase model for fine-tuning (12 transformer blocks, 768-size hidden layer, 12 attention heads, and 110m total parameters). we fine-tuned with dropout probability 0.1 for 3 epochs, and we trained with learning rate 2e-5. train, evaluation, and test splits were generated from our social media dataset using by creating 60/20/20 splits. results. results are shown in ## discussion and future work we have described an approach to encoding the directed social regard (dsr) of authors toward events and actors in their posts, informed by @xcite psychosocial theory of moral disengagement. this helps characterize abuse and harm in online messaging, including the advocacy and opposition to said abuse and harm, by highlighting entities that are associated with aspects associated with moral disengagement. our transformer-based approach uses a multientity aspect-based sentiment analysis (me-absa) treatment to represent and predict dsr across nine psychosocial dimensions. we provide empirical evidence that transformer-based architectures can detect relevant actors and events and then predict human dsr ratings within reasonable preliminary error bounds. limitations and future work. one factor likely reducing the performance of our dsr model is the imbalanced representation of sentiment labels in our dataset. there is a scarcity of examples in our dataset of entities that are associated with some sentiments, particularly moderate to positive sentiments labels and sentiments with low to moderate degrees of intensity. as shown in