ETMS @ IITKGP at S em E val-2022 Task 10: Structured Sentiment Analysis Using A Generative Approach (2022)

Abstract: Structured Sentiment Analysis (SSA) deals with extracting opinion tuples in a text, where each tuple (h, e, t, p) consists of h, the holder, who expresses a sentiment polarity p towards a target t through a sentiment expression e. While prior works explore graph-based or sequence labeling-based approaches for the task, we in this paper present a novel unified generative method to solve SSA, a SemEval2022 shared task. We leverage a BART-based encoder-decoder architecture and suitably modify it to generate, given a sentence, a sequence of opinion tuples. Each generated tuple consists of seven integers respectively representing the indices corresponding to the start and end positions of the holder, target, and expression spans, followed by the sentiment polarity class associated between the target and the sentiment expression. We perform rigorous experiments for both Monolingual and Cross-lingual subtasks, and achieve competitive Sentiment F1 scores on the leaderboard in both settings.

## introduction structured sentiment analysis (ssa) is the task of extracting structured information around sentiment expressions present in text in the form of opinion tuples @xmath0, o 2 , ..., o n }, where each opinion tuple o @xmath1, t, e, p) consists of h, the holder (or source, used interchangeably) who expresses a sentiment polarity p towards a target (or aspect) t using an opinion or sentiment expression e @xcite . prior works @xcite have highlighted the importance of addressing sentiment analysis as a structured prediction problem in order to capture the complete information around various opinions expressed in the text. the task of ssa thus expects to exploit the pairwise interactions between the members of the same opinion tuple during the extraction process. ## related work previous efforts on ssa have primarily focused on two approaches: sequence labeling-based @xcite , and graph-based @xcite . the corresponding scores for both these approaches are considered as baselines by the task organizers. ## conclusion different from prior methods, we in this work present a novel generative approach to tackle the task of structured sentiment analysis. we formulate the task as a structured prediction problem. our bart-based encoder-decoder architecture is trained to predict a sequence of indices corresponding to each opinion tuple present in the text. the generated indices suitably represent the holder, target, and polar expression spans by their start and end token positions, and the sentiment polarity by its corresponding class. as part of semeval 2022 task 10, we participate in both monolingual and crosslingual subtasks, and achieve competitive performance on the leaderboard for both settings. in future, we would like to explore paraphrasingbased generative methods for the task.