Aspect-based Sentiment Analysis with Context Denoising (2024)

Abstract: Given a sentence and a particular aspect term, aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity towards this aspect term, which provides fine-grained analysis on sentiment understanding and it has attracted much attention in recent years. In order to achieve a good performance on ABSA, it is important for a model to appropriately encode contextual information, especially identifying salient features and eliminating noise in the context. To make incorrect predictions, most existing approaches employ powerful text encoders to locate important context features, as well as noises that mislead ABSA models. These approaches determine the noise in the text for ABSA by assigning low weights to context features or directly removing them from model input, which runs the risk of computing wrong weights or eliminating important context information. In this paper, we propose to improve ABSA with context denoising, where three types of word-level information are regarded as noise, namely, lexicographic noise, bag-of-words noise, and syntax noise. We utilize diffusion networks to perform the denoising process to gradually eliminate them so as to better predict sentiment polarities for given aspect terms. Our approach uses task-specific noise rather than the standard stochastic Gaussian noise in the diffusion networks. The experimental results on five widely used ABSA datasets demonstrate the validity and effectiveness of our approach.

## introduction aspect-based sentiment analysis (absa) predicts sentiment polarity of an aspect term in a sentence on the fine-grained level. for example, the sentiments for "environment" and "bar service" in the sentence in figure 1 are positive and negative, † corresponding author. 1 the code and relevant resources used in the paper are available at https://github.com/synlp/asa-cd . figure 1 : an example of aspect-based sentiment analysis, where the sentiment polarities of aspect terms " environment" and "bar service" are positive and negative, respectively. herein, "terrible" serves as context noise in predicting the sentiment of "environment". to perform well on absa, a system needs to have a good representation of the context of a given aspect term and be able to identify salient features that are important in predicting the sentiment of the aspect term. many existing studies use advanced encoders (e.g., bilstm and transformer @xcite ) to capture contextual information for the task and achieve good performance @xcite @xcite @xcite @xcite . some studies incorporate external knowledge, such as lexicon, chunks, and syntactic information @xcite @xcite @xcite @xcite to further improve model performance. these studies generally extract important contextual information from intrinsic or external knowledge and use them as essential hints to predict sentiment polarities and they may suffer from noise the right part presents the mlp decoder to predict the sentiment label. an example input of the context denoising process and the expected noise words (under the syntax noise setting) are provided for better illustration. x and a are the input sentence and aspect term, respectively; u 0 and u t are the initial and t-th step representation of the (sentence, aspect term, sentiment) triple in the noising process, respectively; u 0 and u t are the initial and t-th step representation of the (sentence, aspect term, sentiment) triple in the denoising process, respectively; n is the noise vector; o y is obtained by passing the last vector of u 0 through a fully connected layer. in this paper, we propose an approach to improve absa through diffusion networks, which consists of forward noising and backward denoising processes foot_0 . herein, we propose to use taskspecific noises associated with unimportant words extracted from the context of the running text for absa, rather than the standard stochastic gaussian noise in the forward pass. we consider three types of noises, namely, lexicographic noise, bagof-words noise, and syntax noise, in extracting the noise words. in the denoising process, our approach starts from a noise vector, gradually eliminates the noise from it, and predicts the sentiment polarity. through this process, our approach distinguishes the noises and eliminates their effect on absa in predicting the sentiment, which allows our approach to focus on important features and thus improve model performance. we evaluate our approach on five widely used english absa datasets, where our approach outperforms strong baselines and achieves state-of-the-art results. ## the approach in general, absa is performed as a classification task on sentence-aspect pairs @xcite @xcite . in this paper, we follow the encoding-decoding paradigm with the enhancement of diffusion networks, and the architecture of our approach is illustrated in figure 2 . in the diffusion networks, the noise representation is based on a set of noise words v extracted from the input sentence. thus, our approach for absa is formulated by ## conclusion this paper introduces a novel approach with context denoising for absa, which binds noises to extracted unimportant words so that allows the model to distinguish unimportant context features from the salient ones for predicting the sentiment polarity of a given aspect term. experiments on five english benchmark datasets for absa, namely, lap14, rest14, rest15, rest16, and mams, illustrate the effectiveness of the proposed approach, which outperforms strong baselines and states state-of-the-art performance. further analyses confirm the superiority of utilizing task-specific noise rather than stochastic gaussian noise in diffusion networks for absa. this study also provides novel ideas for tasks that require models to identify essential and non-essential content, where one is able to utilize the diffusion networks to meet the requirement and produce desired outputs. meanwhile, one limitation of this study is that the approach relies on an existing well-performing dependency parser, which is not always available.