Aspect-Category Enhanced Learning with a Neural Coherence Model for Implicit Sentiment Analysis (2023)

Abstract: Aspect-based sentiment analysis (ABSA) has been widely studied since the explosive growth of social networking services. However, the recognition of implicit sentiments that do not contain obvious opinion words remains less explored. In this paper, we propose aspect-category enhanced learning with a neural coherence model (ELCoM). It captures document-level coherence by using contrastive learning, and sentence-level by a hypergraph to mine opinions from explicit sentences to aid implicit sentiment classification. To address the issue of sentences with different sentiment polarities in the same category, we perform cross-category enhancement to offset the impact of anomalous nodes in the hypergraph and obtain sentence representations with enhanced aspect-category. Extensive experiments on benchmark datasets show that the ELCoM achieves state-of-the-art performance. Our source codes and data are released at https://github.com/cuijin-23/ELCoM .

## introduction aspect-based sentiment analysis (absa) has been one of the major research topics in nlp since a large volume of reviews has been accessible via social networking services. much of the previous work on absa has focused on explicit sentiment @xcite @xcite , while implicit sentiment, in which obvious sentiment polarity words do not appear in the sentence but convey sentiments, often appears in reviews. as illustrated in figure 1 , "the food here is rather good, but only if you like to wait for it." in s 1 , cannot be clearly identified as "negative" with respect to the aspect category "service," because it does not include any opinion words related to it. even less work on implicit sentiment mainly leverages intrasentential information to exploit effective contexts, for example, syntactic information from dependency trees which results in insufficient representations capturing their contexts. one feasible solution is to leverage one aspect of document quality: a document forms coherence if it is well-written and easy to understand. many coherence models such as entity-based approaches @xcite , and neural coherence models @xcite have been proposed and applied to various nlp tasks. however, it is often the case that each sentence in a review includes several different aspect-categories, and even the same aspectcategory within a review expresses different sentiment polarities. as illustrated in bold lines in figure 1 , s 1 has two different aspect-categories, "food" and "service," with different polarities. likewise, "food" in s 1 is positive, while that of s 5 shows negative polarity. much of the existing coherence models obtained from the distribution of entities or adjacent sentences are inadequate and make it hard to predict an accurate polarity of implicit sentiment. motivated by the issue mentioned above, we propose an aspect-category enhanced learning with a neural coherence model (elcom) that leverages coherence information for absa, especially ben-eficial for implicit sentiment classification. on the one hand, observing from the semeval-15 and semeval-16 absa datasets, (i) more than 50% of the reviews had the same sentiment polarity regardless of different aspect-categories, and (ii) more than 70% of reviews had the same sentiment polarity if they had the same aspect-category. these observations indicate that (i) the reviewer's opinions are likely to be preserved throughout the review and (ii) the aspect-category is a strong clue for capturing sentence-level coherence to aid implicit sentiment classification. we thus utilize documentlevel coherence to deal with (i) and exploit the relationships among sentences and aspect-categories by utilizing a hypergraph for (ii). on the other hand, the rest of the 20∼30% reviews include opposite polarities with each other even in the same category, while these reviews preserve coherence. to compensate for the dilemma of the side effect, we perform cross-category enhancements in the hypergraph. more specifically, we utilize a self-attention (sa) filtering to offset the impact of anomalous nodes, apply retrieval-based attention (rba) technique @xcite to learn the enhanced embedding of the aspect term, and finally obtain sentence representations with enhanced aspect-category. the main contributions of our work can be summarized as follows: (1) we propose an elcom that learns document-level coherence by using contrastive learning and sentence-level by hypergraph for mining opinions to aid implicit sentiment classification; (2) we propose cross-category enhancements on node embedding to offset the impact of anomalous nodes to correctly identify the sentiment of the same categories but have different polarities; and (3) extensive experiments on semeval-2015 and 2016 show that our method achieves state-ofthe-art performance. ## task definition let @xmath0, where the i-th sentence s @xmath1. let a s @xmath2. each a t s i consists of a pair of the aspect-category and term. let @xmath3, negative, neutral} be a label set of sentiment polarities. the goal of the absa task is, for the given t-th aspect in s i , to predict the sentiment label l(a t s i ). ## approach the overall architecture of the elcom is illustrated in figure 2 . it comprises three key steps: (1) representation learning with xlnet, (2) coherence modeling (com) to capture document-level and sentence-level coherence, and (3) cross-category enhancement to mitigate the influence of anomalous nodes. ## conclusion we proposed aspect-category enhanced learning with a neural coherence model (elcom) for implicit sentiment analysis. to mine opinions from explicit sentences to aid implicit sentiment classification, elcom captures document-level coherence by using contrastive learning, and sentence-level by a hypergraph. to further offset the impact of anomalous nodes in hyperedges, we proposed a cross-category enhancement on node embeddings. extensive experiments have shown that the el-com achieves competitive performance against state-of-the-art sentiment analysis methods. future work includes, (i) improving the elcom by introducing a pre-training large sentiment corpus, and (ii) extending the elcom to simultaneously detect aspect-categories and their polarities @xcite . original review [review @xmath4: average to good thai food, but terrible delivery. opinions @xmath5: i've waited over one hour for food. opinions @xmath6: they were very abrupt with me when i called and actually claimed the food was late because they were out of rice. opinions @xmath7: a thai restaurant out of rice during dinner? opinions @xmath8: the food arrived 20 minutes after i called, cold and soggy. disordered review text s 4 : a thai restaurant out of rice during dinner? text s 1 : average to good thai food, but terrible delivery. text s 5 : the food arrived 20 minutes after i called, cold and soggy. text s 3 : they were very abrupt with me when i called and actually claimed the food was late because they were out of rice. text s 2 : i've waited over one hour for food.