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

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.

Toward Transformer Fusions for Chinese Sentiment Intensity Prediction in Valence-Arousal Dimensions

“…Recently, BERT-like transformer architectures have been widely used for dimensional sentiment analysis. The pretrained and case-sensitive BERT-base model was fine-tuned to predict the degree of sentiment intensity associated with multiple entities for aspect-based sentiment analysis [34]. A multi-task architecture based on the RoBERTa transformer was proposed to predict empathy and distress scores [35].…”

“…This section describes the existing methods for sentiment intensity prediction, including lexicon-based [4,[10][11][12][13][14][15][16], regression-based [17][18][19][20][21][22], neural-network-based [23][24][25][26][27][28][29][30][31][32][33] and transformer-based [34][35][36][37][38][39][40][41][42][43][49][50][51] approaches.…”

“…In general, sentiment intensity prediction methods can be summarized into four types: lexicon-based [4,[10][11][12][13][14][15][16], regression-based [17][18][19][20][21][22], neural-network-based [23][24][25][26][27][28][29][30][31][32][33], and transformer-based [34][35][36][37][38][39][40][41][42][43]. The lexicon-based approaches provide baseline results for reference, while transformer-based models have usually achieved promising results in the valence-arousal dimensions [9].…”

GCAT-GTCU: Graph-Connected Attention Network and Gate Than Change Unit for Aspect-Level Sentiment Analysis

“…In addition, semantic information for aspect-level sentiment analysis can be learned through sentence and aspect interaction [23][24][25]. In sentences with the same aspect and sentiment prediction polarity, different semantic expressions of the same aspect will appear, and learning sentiment knowledge from different semantic expressions can improve the generalization of the model, nowadays there are already many methods to extract semantic information from only one sentence [26][27][28], how to extract deep-level features and semantic information in multiple sentences and achieve interaction to improve the overall performance of the model is a problem worth exploring.…”