Comparative Opinion Quintuple Extraction from Product Reviews (2021)

Abstract: As an important task in opinion mining, comparative opinion mining aims to identify comparative sentences from product reviews, extract the comparative elements, and obtain the corresponding comparative opinion tuples. However, most previous studies simply regarded comparative tuple extraction as comparative element extraction, but ignored the fact that many comparative sentences may contain multiple comparisons. The comparative opinion tuples defined in these studies also failed to explicitly provide comparative preferences. To address these limitations, in this work we first introduce a new Comparative Opinion Quintuple Extraction (COQE) task, to identify comparative sentences from product reviews and extract all comparative opinion quintuples (Subject, Object, Comparative Aspect, Comparative Opinion, Comparative Preference). Secondly, based on the existing comparative opinion mining corpora, we make supplementary annotations and construct three datasets for the COQE task. Finally, we benchmark the COQE task by proposing a new BERT-based multi-stage approach as well as three baseline systems extended from previous methods. %The new approach significantly outperforms three baseline systems on three datasets and represents a strong benchmark for COQE. Experimental results show that the new approach significantly outperforms three baseline systems on three datasets for the COQE task.

## introduction fine-grained opinion mining from product reviews has received considerable attention in the last decade. as around 10% of product reviews contain at least one comparison @xcite , it is therefore crucial to extract and analyze these comparative sentences to detect public opinions towards the compared entities and aspects. as the pioneering work for this direction, jindal and @xcite proposed the comparative sentence mining (csm) task which first identifies comparative sentences from reviews, and extracts pre-defined comparative quintuples, i.e., (subject, object, comparative aspect, relation word, comparison type), from the identified comparative sentences. for example, given a sentence "g6 has a worse zoom than g7, but g6's battery was more reliable than g7", one comparative quintuple is (g6, g7, battery, more, non-equal gradable). in their work they assumed that a comparative sentence contains only one comparative relation, and treated the comparative quintuple extraction task as a comparative element extraction (cee) problem. however, in real scenarios, a large number of comparative sentences contain more than one comparative relation. for example, 17.6% of the comparative sentences in the camera domain @xcite have at least two comparative relations. in this situation, simply applying cee cannot extract comparative quintuples effectively. moreover, in their definition, the relation word sometimes fails to explicitly reflect the comparative preference. for example, "more" in the above sentence is ambiguous, since it may refer to "more reliable" or "more expensive". some recent studies @xcite proposed a new task named comparative preference classification (cpc), to identify the explicit comparative preferences (e.g., better, worse, none) between the subject entity and the object entity. however, the cpc task requires that the subject and object entities have been annotated, which largely hinders its applications in real scenarios. to address the limitations of cee and cpc, we introduce a new comparative opinion quintuple extraction (coqe) task, with the emphasis on the identification of comparative sentences, and the g6 has a worse zoom than g7, but g6's battery was more reliable than g7. ## related work as a branch of aspect-based sentiment analysis, comparative sentence mining was first proposed by @xcite to first identify comparative sentences (csi) from reviews, and extracts pre-defined comparative quintuples, i.e., (subject, object, comparative aspect, relation word, comparison type) from the identified comparative sentences. they assumed that a comparative sentence contains only one comparative relation, and regarded comparative quintuple extraction as a comparative element extraction (cee) problem. this ignored the fact that a large percentage of comparative sentences contain more than one comparison. for the csi task, @xcite @xcite designed keyword-based or syntactic-based rules to identify comparative sentences in product reviews and scientific articles. @xcite @xcite employed a class sequential rule (csr) method to mine sequence rules and use them as features of statistical classifiers. for the cee task, @xcite and @xcite employed a label sequential rule (lsr) method to extract comparative elements. @xcite @xcite extracted comparative elements based on conditional random field (crf). @xcite and @xcite further employed semantic role labeling (srl) to extract comparative elements. @xcite proposed a lstm-crf neural network to extract comparative elements. in recent years, @xcite proposed a comparative preference classification (cpc) task, to predict the preference (better, worse, none) between two annotated entities. @xcite further proposed a graph attention network for this task. however, cpc requires to annotate two compared entities in advance, which greatly limits its application in real scenes. in comparison, the coqe task proposed in this work focuses on identification of comparative sentences, and the extraction of all the comparative opinion quintuples from the comparative sentence, rather than comparative element extraction only. we support comparative quintuple extraction when a sentence contains multiple comparisons. secondly, we re-define the comparative quintuple by incorporating comparative preference, and jointly perform comparative tuple extraction and comparative preference classification. finally, most of the previous models for comparative opinion mining were based on rule methods or traditional machine learning methods. we establish a multi-stage deep learning approach for our task and significantly improved the performance of both cee and coqe. it is also worth noting that some recent studies on opinion tuple extraction @xcite and quadruple extraction @xcite have been proposed in traditional aspectbased sentiment analysis. our work can be viewed as their extension from absolute opinion mining to comparative opinion mining. ## approach as stated in the task definition, coqe is a challenging task that includes four-element extraction, one-element classification, and five-element combinations. to tackle the task, we propose a multistage neural network framework, in which the first stage is to identify comparative sentences and extract comparative elements, the second stage is to combine and filter the extracted four comparative elements (sub, obj , ca, co) to obtain valid comparative quadruples, and the third stage is to further classify each comparative quadruple into a pre-defined preference category, and obtain all the comparative opinion quintuples. for the sentence in ## conclusions and future work in this work, we introduce a new comparative opinion quintuple extraction (coqe) task, to identify comparative sentences from reviews, and extract all comparative opinion quintuples each of which includes subject, object, comparative aspect, comparative opinion and comparative preference. we construct three datasets for the task, and benchmark the task by proposing a new multistage neural network approach which shows significant advantages in comparison with baseline systems extended from previous methods. in the future work, we would like to consider more sophisticated approaches, for example, end-to-end deep learning models, for coqe.