Opinion Tree Parsing for Aspect-based Sentiment Analysis (2023)

Abstract: Extracting sentiment elements using pre-trained generative models has recently led to large improvements in aspect-based sentiment analysis benchmarks. These models avoid explicit modeling of structure between sentiment elements, which are succinct yet lack desirable properties such as structure well-formedness guarantees or built-in elements alignments. In this study, we propose an opinion tree parsing model, aiming to parse all the sentiment elements from an opinion tree, which can explicitly reveal a more comprehensive and complete aspect-level sentiment structure. In particular, we first introduce a novel context-free opinion grammar to normalize the sentiment structure. We then employ a neural chart-based opinion tree parser to fully explore the correlations among sentiment elements and parse them in the opinion tree form. Extensive experiments show the superiority of our proposed model and the capacity of the opinion tree parser with the proposed context-free opinion grammar. More importantly, our model is much faster than previous models.

## introduction aspect-based sentiment analysis (absa) has drawn increasing attention in the community, which includes four subtasks: aspect term extraction, opinion term extraction, aspect term category classification and aspect-level sentiment classification. the first two subtasks aim to extract the aspect term and the opinion term appearing in one sentence. the goals of the remaining two subtasks are to detect the category and sentiment polarity towards the extracted aspect term. previously, most absa tasks are formulated as either sequence-level @xcite @xcite or token-level classification * corresponding author problems @xcite . however, these methods usually suffer severely from error propagation because the overall prediction performance hinges on the accuracy of every step @xcite . therefore, recent studies tackle the absa problem with a unified generative approach. for example, they treat the class index @xcite or the desired sentiment element sequence @xcite as the target of generation model. more recently, @xcite addresses the importance of correlations among sentiment elements (e.g., aspect term, opinion term), and proposes an opinion tree generation model, which aims to jointly detect all sentiment elements in a tree structure. the major weakness of generative approaches is the training and inference efficiency, they always need large-scale computing resources. in addition, these generative approaches lack certain desirable properties. there are no structural guarantees of structure well-formedness, i.e. the model may predict strings that can not be decoded into valid opinion trees, and post-processing is required. furthermore, predicting linearizations ignores the implicit alignments among sentiment elements, which provide a strong inductive bias. as shown in figure 1 , we convert all the sentiment elements into an opinion tree and design a neural chart-based opinion tree parser to address these shortcomings. the opinion tree parser is much simpler and faster than generative models. it scores each span independently and performs a global search over all possible trees to find the highest-score opinion tree @xcite . it explicitly models tree structural constraints through span-based searching and yield alignments by construction, thus guaranteeing tree structure well-formedness. one challenge to the above is that not all the review texts contain standard sentiment quadruplets (i.e., aspect term, opinion term, aspect category, and polarity) which can be easily formed in an opinion tree @xcite . for example, there may be more than one opinion term correlated with an aspect term and vice versa. in addition, aspect or opinion terms might be implicit. according to our statistics, such irregular situations appear in more than half of review texts. in this study, we propose a novel context-free opinion grammar to tackle these challenges. the grammar is generalized and well-designed, it is used to normalize the sentiment elements into a comprehensive and complete opinion tree. furthermore, it contains four kinds of conditional rules, i.e., one-to-many, mono-implicit, bi-implicit, cross-mapping, which are used to solve the irregular situations in opinion tree parsing. the detailed evaluation shows that our model significantly advances the state-of-the-art performance on several benchmark datasets. in addition, the empirical studies also indicate that the proposed opinion tree parser with context-free opinion grammar is more effective in capturing the sentiment structure than generative models. more importantly, our model is much faster than previous models. ## related work as a complex and challenging task, aspect-based sentiment analysis (absa) consists of numerous sub-tasks. the researches on absa generally follow a route from handling single sub-task to complex compositions of them. the fundamental subtasks focus on the prediction of a single sentiment element, such as extracting the aspect term @xcite @xcite , detecting the mentioned aspect category @xcite , and predicting the sentiment polarity for a given aspect @xcite @xcite @xcite . since the sentiment elements are natural correlated, many studies focus on exploring the joint extraction of pairwise sentiment elements, including aspect and opinion term extraction @xcite ; aspect term extraction and its polarity detection @xcite ; aspect category and polarity detection @xcite . furthermore, recent studies also employed end-toend models to extract all the sentiment elements in triplet or quadruple format @xcite @xcite @xcite . more recently, studies using pre-trained encoderdecoder language models show great improvements in absa @xcite . they either treated the class index @xcite or the desired sentiment element sequence @xcite as the target of the generation model. in addition, @xcite addressed the importance of correlations among sentiment elements, and proposed an opinion tree generation model, which aims to jointly detect all sentiment elements in a tree structure. however, the generative models always need large-scale computing resources, they also cannot guarantee the structure well-formedness, and ignores the implicit alignments among sentiment elements. in this study, we propose a novel opinion tree parser, which aims to model and parse the sentiment elements from the opinion tree structure. the proposed model shows significant advantages in both decoding efficiency and performance as it is much faster and more effective in capturing the sentiment structure than generative models. furthermore, we design a context-free opinion grammar to normalize the opinion tree structure, and improve parser's applicability decisions for complex compounding phenomena. ## overview of proposed model aspect-based sentiment analysis aims to extract all kinds of sentiment elements and their relations from review text. basically, there are four kinds of sentiment elements in the review text: aspect term denotes an entity and its aspect indicating the opinion target, which is normally a word or phrase in the text; aspect category represents a unique predefined category for the aspect in a particular domain; opinion term refers the subjective statement on an aspect, which is normally a subjective word or phrase in the text; polarity is the prede- the surface is extremely smooth, but the apps are hard to use. chart-based opinion tree parser @xmath0, but the fined semantic orientation (e.g., positive, negative, or neutral) toward the aspect. as shown in figure 2 , we convert all the sentiment elements into an opinion tree, and we design a chart-based opinion tree parser with context-free opinion grammar to parse the opinion tree from review text. in particular, we firstly propose a contextfree opinion grammar to normalize the sentiment elements into an opinion tree. we then perform a neural chart-based opinion tree parser to parse the opinion tree structure from a given review text. since all the sentiment elements are normalized into the opinion tree, it is easy to recover them from the tree. in the next two sections, we will discuss the context-free opinion grammar and the opinion tree parser in detail. ## context-free opinion grammar in this study, we propose a novel context-free opinion grammar to normalize the opinion tree structure. in the below of this section, we first introduce basic definitions of context-free opinion grammar. after that, we give some conditional rules to solve irregular situations and show some examples to illustrate the effectiveness of proposed grammar. ## opinion tree parser in this study, we employ a neural chart-based opinion tree parser to parse sentiment elements from the opinion tree structure. as shown in figure 4 , the opinion tree parser follows an encoder-decoder architecture @xcite @xcite . it scores each span independently and performs a global search over all possible trees to find the highest-score opinion tree. in particular, the process of opinion tree parsing can be separated into two stages: context-aware encoding and chart-based decoding, we will discuss these in the below subsections. ## experiments in this section, we introduce the dataset used for evaluation and the baseline methods employed for comparison. we then report the experimental results conducted from different perspectives. ## analysis and discussion in this section, we give some analysis and discussion to show the effectiveness of proposed opinion tree parser for aspect-based sentiment analysis. ## conclusion in this study, we propose a novel opinion tree parsing model, aiming to parse all the sentiment elements into an opinion tree, which can reveal a more comprehensive and complete aspect-level sentiment structure. in particular, we first introduce a novel context-free opinion grammar to normalize the opinion structure. we then employ a neural chart-based opinion tree parser to fully explore the correlations among sentiment elements and parse them in the opinion tree form. detailed evaluation shows that our model significantly advances the state-of-the-art performance on several benchmarks. the empirical studies also show that the proposed opinion tree parser with context-free opinion grammar is more effective in capturing the opinion tree structure than generative models with a remarkable advantage in computation cost.