Modeling Aspect Correlation for Aspect-based Sentiment Analysis via Recurrent Inverse Learning Guidance (2022)

Abstract: Aspect-based sentiment analysis (ABSA) aims to distinguish sentiment polarity of every specific aspect in a given sentence. Previous researches have realized the importance of interactive learning with context and aspects. However, these methods are ill-studied to learn complex sentence with multiple aspects due to overlapped polarity feature. And they do not consider the correlation between aspects to distinguish overlapped feature. In order to solve this problem, we propose a new method called Recurrent Inverse Learning Guided Network (RILGNet). Our RILGNet has two points to improve the modeling of aspect correlation and the selecting of aspect feature. First, we use Recurrent Mechanism to improve the joint representation of aspects, which enhances the aspect correlation modeling iteratively. Second, we propose Inverse Learning Guidance to improve the selection of aspect feature by considering aspect correlation, which provides more useful information to determine polarity. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of RILGNet, and we further prove that RILGNet is state-of-the-art method in multiaspect scenarios.

## introduction aspect-based sentiment analysis (absa) aims to distinguish the orientation of sentiment existed on every aspect @xcite . for example, in the sentence: "food is usually very good, though i wonder about freshness of raw vegetables", aspect "raw vegetables" has negative polarity and "food" has positive polarity. absa task consists of two subtasks called aspect extraction and aspect sentiment classification @xcite . in this paper, we focus on aspect sentiment classification task and assume that aspects are known @xcite . as shown in above case, multiple sentiment polarities could exist in one sentence, which leads to the overlapping use of multiple aspects when predicting the polarity of current aspect @xcite . we can see that sentiment polarity of "food" comes from word "good" which implies positive. "raw vegetables" does not have any words linked in above case. however, conjunction "though" determines sentiment polarity of "raw vegetables" which implies negative. feature about "good" is overlapped by these two aspects and conjunction "though" leads to different sentiment polarity between "food" and "raw vegetables". in this case, two aspects exhibit high correlation. therefore, modeling the correlation between multiple aspects is a suitable way to distinguish overlapped feature. from the above case, it is not difficult to see that aspect correlation comes from these sources: aspects, their sentiment polarities and conjunctions. conjunction establishes correlation between aspects. if model learns correlation between aspects, it can make right predition of "raw vegetables" from the feature of "food". previous works have been devoted to introduce attention mechanism into neural network and great progress has been made @xcite . self-attention-based models such as bert @xcite have been applied to absa by using bert as an encoder @xcite . to handle the overlapped aspect feature, some works aim at decoupling overlapped feature for every aspect. @xcite uses a capsule network and interactive attention to decouple overlapped feature for predicted aspect. in another way, @xcite tries to model inter-aspect relation by learning every aspect feature respectively. however, these methods only use aspect (e.g., "food") and not use sentiment polarity (e.g., "positive"). aspect and sentiment polarity both reflect aspect correlation. considering aspect correlation is an ef-fective way to determine the polarity of aspect that is not easy to find linked words. we find that inverse learning @xcite is a suitable way to excavate the correlation from aspect and sentiment polarity because it learns from input (aspect and sentiment polarity) to output (generated sentence). and generated sentence is same with aspect and sentiment polarity @xcite in correlation. in this paper, we design a recurrent inverse learning guided network (rilgnet) to improve the performance of absa task through modeling aspect correlation. our rilgnet has two point to improve aspect correlation modeling and aspect feature selecting. first, we use recurrent mechanism @xcite to learn aspect joint representation and model aspect correlation. and we use the correlation and representation to make current prediction. recurrent mechanism can find clear stated aspect polarities ("food" and positive in above sentence), and then use them to re-find indirect aspect polarities ("raw vegetables" and negative). for this purpose, recurrent mechanism is a suitable way. second, we use inverse learning guidance (ilg) to excavate the correlation from aspects and sentiment polarities. we use inverse learning to generate a sentence by using aspectpolarity combination. we consider the difference between the given sentence and the generated sentence embedding by calculating mean squared error (mse) and its gradient. this gradient expresses difference in correlation between the prediction and the ground-truth. we fuse this gradient to aspect joint representation, which makes the selection of aspect feature focus more on high correlation aspects. we show that rilgnet achieves 83.70% and outperforms state-of-the-art method by 1.34% on average in semeval 2014 two distinct domains: laptop and restaurant. and we also prove that rilgnet is state-of-the-art method in multi-aspect scenarios. the main contributions are as follows: (1) ril-gnet improves the performance of absa task by modeling aspect correlation from aspect and sentiment polarity. (2) we design an ilg to get guidance about how aspect joint representations are modified and how features are selected from current prediction. (3) experimental results show that our method outperforms state-of-the-art method on semeval 2014 datasets, especially in multi-aspect scenarios. ## related works the traditional methods of absa find optional words about aspect in sentence, and then get the corresponding sentiment through special designed aspect features such as sentiment lexicon, n-grams, and dependency information, which is labor-intensive @xcite . meanwhile, the quality of feature directly affects classification accuracy. motivated by attention mechanism in the field of deep learning, some absa's works focus on integrating attention into neural network, such as rnns @xcite and memory networks @xcite , to learn attention distribution of different aspects and extract aspect-aware sentence representation. most recently, self-attention-based models such as bert @xcite have been applied to absa by using bert as the embedding layer @xcite . these works effort to focus on understanding the context based on aspect and have achieved great performance improvement. besides the above works, chen et al. (2020); zhang and qian (2020); @xcite ; @xcite ; @xcite use syntactic information and improved semantic understanding to improve end-to-end performance. graph-based models are applied in absa task by utilizing word relations through dependency parses to facilitate the task with better semantic guidance for analyzing context and aspect words @xcite @xcite . they realize that the overlapped feature about multi-aspect is ignored when the model predicts current aspect. @xcite calculates the aspect-aware sentence representation for every aspect in a sentence, which models the inter-aspect relation in a sentence for handling the overlapped feature from aspect information. @xcite uses a capsule network and interactive attention to decouple overlapped feature to predicted aspect. @xcite uses mutual enhanced transformation to obtain more effective representation of aspect and context for handling overlapped feature. however, unlike our proposed method, they only use aspect information without sentiment polarity information. aspect and sentiment polarity both reflect aspect correlation. food is usually very good, though i wonder about freshness of raw vegetables. ## conclusion in this paper, we propose a recurrent inverse learning guidance network (rilgnet) to solve absa task by modeling aspect correlation. firstly, in order to solve the problem of overlapped aspect feature, we design a recurrent meshanism to learn aspect joint representation and model aspect correlation. secondly, rilgnet uses inverse learning guidance to give feedback on how current predition reflect aspect correlation by gradient iteratively. experimental results demonstrate the effectiveness of rilgnet for absa. and ril-gnet also performs well in multi-aspect scenarios especially. above all, modeling aspect correlation or label correlation will be useful for other nlp tasks.