Label Correction Model for Aspect-based Sentiment Analysis (2020)

Abstract: Aspect-based sentiment analysis includes opinion aspect extraction and aspect sentiment classification. Researchers have attempted to discover the relationship between these two sub-tasks and have proposed the joint model for solving aspect-based sentiment analysis. However, they ignore a phenomenon: aspect boundary label and sentiment label of the same word can correct each other. To exploit this phenomenon, we propose a novel deep learning model named the label correction model. Specifically, given an input sentence, our model first predicts the aspect boundary label sequence and sentiment label sequence, then re-predicts the aspect boundary (sentiment) label sequence using the embeddings of the previously predicted sentiment (aspect boundary) label. The goal of the re-prediction operation (can be repeated multiple times) is to use the information of the sentiment (aspect boundary) label to correct the wrong aspect boundary (sentiment) label. Moreover, we explore two ways of using label embeddings: add and gate mechanism. We evaluate our model on three benchmark datasets. Experimental results verify that our model achieves state-of-the-art performance compared with several baselines.

## introduction aspect-based sentiment analysis (absa) aims to extract the opinion aspects mentioned in the sentence and to predict the sentiment of each aspect @xcite . for example, in the review "average to good thai food , but terrible delivery .", thai food and delivery are aspects, with positive and negative as their corresponding sentiments. in the literature, absa is usually divided into two sub-tasks, namely, opinion aspect extraction and aspect sentiment classification. the goal of opinion aspect extraction is to extract all aspects present in a sentence. early work @xcite focuses on detecting the pre-defined aspects in a sentence. then, some work @xcite regards the opinion aspect extraction as a sequence labeling task. the goal of aspect sentiment classification is to predict the sentiment of the given opinion aspect, and this sub-task has drawn growing research interests over the past few years @xcite @xcite . however, most previous work focuses on only one of the sub-tasks. this limits the practical application of absa. to apply the existing methods of two sub-tasks in practical applications (i.e., not only extracting aspects but also predicting their sentiments), the most common way is to pipeline the methods of two sub-tasks together. to further promote the resolution of absa, researchers have attempted to discover the relationship between these two sub-tasks and have developed the joint models @xcite . they utilize a set of aspect boundary labels (e.g., b, i, e, s, o) and a set of sentiment labels (e.g., pos, neg, neu and o) to make the models of two sub-tasks jointly trained. in other words, absa is modeled as an extension to opinion aspect extraction, where an extra sentiment label is assigned to each word, in addition to the aspect boundary label. to summarize, we make the following contributions in this paper: ## related work aspect-based sentiment analysis is usually divided into two sub-tasks, namely, opinion aspect extraction and aspect sentiment classification. ## conclusions in this paper, we investigate aspect-based sentiment analysis tasks, which can be formulated as two sequence labeling problems with a set of aspect boundary labels and a set of sentiment labels. we observe that the aspect boundary label and sentiment label of the same word can correct each other. thus, we propose the label correction model that exploits this observation to improve performance. moreover, we introduce two ways (add and gate mechanism) to make use of label information. we evaluate our proposed model on the three benchmark datasets. extensive experiments show that our proposed model achieves superior performance.