ECNU _ ICA at S em E val-2022 Task 10: A Simple and Unified Model for Monolingual and Crosslingual Structured Sentiment Analysis (2022)

Abstract: Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint. In this paper, we focus on the structured sentiment analysis task that is released on SemEval-2022 Task 10. The task aims to extract the structured sentiment information (e.g., holder, target, expression and sentiment polarity) in a text. We propose a simple and unified model for both the monolingual and crosslingual structured sentiment analysis tasks. We translate this task into an event extraction task by regrading the expression as the trigger word and the other elements as the arguments of the event. Particularly, we first extract the expression by judging its start and end indices. Then, to consider the expression, we design a conditional layer normalization algorithm to extract the holder and target based on the extracted expression. Finally, we infer the sentiment polarity based on the extracted structured information. Pre-trained language models are utilized to obtain the text representation. We conduct the experiments on seven datasets in five languages. It attracted 233 submissions in monolingual subtask and crosslingual subtask from 32 teams. Finally, we obtain the top 5 place on crosslingual tasks.

## introduction the identification of sentiment in the text is an important field of study. users' opinions on products, events, topics, and so on are valuable for both the company and government to improve products or policies. recently, more and more researchers focus on fine-grained sentiment analysis tasks, such as structured sentiment analysis, which can be formulated into tuple extraction from the context @xcite . we focus on structured sentiment analysis, which is released by semeval 2022 task 10. formally, the task aims to extract all of the opinion tuples @xmath0, ..., o n } in a text. a tuple (h, t, e, p) can represent the structure of sentiment in context. as shown in figure 1 , "some other " (h) is a holder who expresses a polarity "positive" (p) figure 1 : a structured sentiment graph is composed of a holder, target, sentiment expression, their relationships and a polarity attribute. holders and targets can be null. structured sentiment analysis can be resolved down into four sub-tasks: a) expression extraction, b) target extraction, c) holder extraction, and d) sentiment polarity classification. previous work on information extraction has used pipeline methods that extract the holders, targets, and expressions (tasks a-c). most of the works conduct experiments on the mpqa dataset @xcite . @xcite ; @xcite used crfs with the extracted features (e.g., named-entity tagger, sentiment lexicons, and dependency parsers), which results in a very strong baseline. for a complicated task with a small size of the training data, these feature-based mechanism learning techniques often still perform better than neural-based models, such as bi-lstms @xcite . the end-to-end approaches have shown some potential by learning the relationships among the multiple subtasks @xcite . however, all of these studies ignore the sentiment polarity classification subtask. @xcite regarded the structured sentiment analysis task as a dependency parsing task to model the relationships among the elements. in this paper, we transform this task into an event extraction framework. because we found the expression word is the same as the trigger word in the event extraction, both of them can uniquely represent the whole structured tuple. besides, we regard the other elements (e.g., holder and targets) as the arguments of the sentiment structured information. above all, we propose a pipeline model for this task. the key problem for this task is how to model the relationships among the targets, holders and expressions. to take the expressions into account, we introduce a conditional layer normalization method to extract the holders and targets w.r.t. the expression. our simple and unified model is appropriate for both monolingual and crosslingual tasks. experiments on datasets of monolingual and crosslingual tasks show our approach is capable to improve performance significantly on structured sentiment analysis as event extraction. ## our approach in this paper, we propose a simple and unified model to extract the structured sentiment analysis. this section describes how we redefine the sentiment classification task to an event extraction task, detail the pipeline method. fig. 3 shows the framework of our model, which consists of three components, expression extraction, arguments extraction (including holder extraction and target extraction), sentiment classification. first, we extract the expression based on (multi-lingual) pretrained language models (plms) (see subsection 3.2). second, we design a conditional layer normalization strategy to extract the holders and targets by incorporating the expression. finally, we perform sentiment classification tasks based on the sentence representation learned by the previous part (see subsection 3.3 and 3.4). ## experiments in this section, we first give the experimental setup, including datasets, implementation details and evaluation metrics (section 4.1). then, we present the experimental results and analysis on both the monolingual and multilingual settings (see section 4.2). ## conclusions in this paper, we propose a simple and unified model for both the monolingual and crosslingual structured sentiment analysis tasks. different from the previous studies, we transform this task into an event extraction task. mainly, we first design an expression extraction for extracting the expression, just like extracting the trigger words in the event extraction task. then we predict the holder and target based on the extraction results of the previous step. the model performs well on seven datasets in five languages.