The aspect-based sentiment analysis (ABSA) consists of two conceptual tasks, namely an aspect extraction and an aspect sentiment classification. Rather than considering the tasks separately, we build an end-to-end ABSA solution. Previous works in ABSA tasks did not fully leverage the importance of syntactical information. Hence, the aspect extraction model often failed to detect the boundaries of multi-word aspect terms. On the other hand, the aspect sentiment classifier was unable to account for the syntactical correlation between aspect terms and the context words. This paper explores the grammatical aspect of the sentence and employs the self-attention mechanism for syntactical learning. We combine part-of-speech embeddings, dependency-based embeddings and contextualized embeddings (e.g. BERT, RoBERTa) to enhance the performance of the aspect extractor. We also propose the syntactic relative distance to de-emphasize the adverse effects of unrelated words, having weak syntactic connection with the aspect terms. This increases the accuracy of the aspect sentiment classifier. Our solutions outperform the state-of-the-art models on SemEval-2014 dataset in both two subtasks.

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Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis.

A multi-task learning model for Chinese-oriented aspect polarity classification and aspect term extraction

Aspect-based (aspect-level) sentiment analysis is an important task in fine-grained sentiment analysis, which aims to automatically infer the sentiment towards an aspect in its context Previous studies have shown that utilizing the attention-based method can effectively improve the accuracy of the aspect-based sentiment analysis Despite the outstanding progress, aspect-based sentiment analysis in the real-world remains several challenges (1) The current attention-based method may cause a given aspect to incorrectly focus on syntactically unrelated words (2) Conventional methods fail to identify the sentiment with the special sentence structure, such as double negatives (3) Most of the studies leverage only one vector to represent context and target However, utilizing one vector to represent the sentence is limited, as the natural languages are delicate and complex In this paper, we propose a knowledge guided capsule network (KGCapsAN), which can address the above deficiencies Our method is composed of two parts, a Bi-LSTM network and a capsule attention network The capsule attention network implements the routing method by attention mechanism Moreover, we utilize two prior knowledge to guide the capsule attention process, which are syntactical and n-gram structures Extensive experiments are conducted on six datasets, and the results show that the proposed method yields the state-of-the-art

Knowledge Guided Capsule Attention Network for Aspect-Based Sentiment Analysis

Targeted sentiment classification predicts the sentiment polarity on given target mentions in input texts. Dominant methods employ neural networks for encoding the input sentence and extracting relations between target mentions and their contexts. Recently, graph neural network has been investigated for integrating dependency syntax for the task, achieving the state-of-the-art results. However, existing methods do not consider dependency label information, which can be intuitively useful. To solve the problem, we investigate a novel relational graph attention network that integrates typed syntactic dependency information. Results on standard benchmarks show that our method can effectively leverage label information for improving targeted sentiment classification performances. Our final model significantly outperforms state-of-the-art syntax-based approaches.

Investigating Typed Syntactic Dependencies for Targeted Sentiment Classification Using Graph Attention Neural Network

Aspect-based sentiment analysis (ABSA) task is a multi-grained task of natural language processing and consists of two subtasks: aspect term extraction (ATE) and aspect polarity classification (APC). Most of the existing work focuses on the subtask of aspect term polarity inferring and ignores the significance of aspect term extraction. Besides, the existing researches do not pay attention to the research of the Chinese-oriented ABSA task. Based on the local context focus (LCF) mechanism, this paper firstly proposes a multi-task learning model for Chinese-oriented aspect-based sentiment analysis, namely LCF-ATEPC. Compared with existing models, this model equips the capability of extracting aspect term and inferring aspect term polarity synchronously, moreover, this model is effective to analyze both Chinese and English comments simultaneously and the experiment on a multilingual mixed dataset proved its availability. By integrating the domain-adapted BERT model, the LCF-ATEPC model achieved the state-of-the-art performance of aspect term extraction and aspect polarity classification in four Chinese review datasets. Besides, the experimental results on the most commonly used SemEval-2014 task4 Restaurant and Laptop datasets outperform the state-of-the-art performance on the ATE and APC subtask.

A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction

Aspect-based sentiment analysis (ABSA) is a hot and significant task of natural language processing, which is composed of two subtasks, the aspect term extraction (ATE) and aspect polarity classification (APC). Previous researches generally studied two subtasks independently and designed neural network models for ATE and APC respectively. However, it integrates various manual features into the model, which will consume plenty of computing resources and labor. Moreover, the quality of the ATE results will affect the performance of APC. This paper proposes a multi-task learning model based on dual auxiliary labels for ATE and APC. In this paper, general IOB labels, and sentimental IOB labels are equipped to efficiently solve both ATE and APC tasks without manual features adopted. Experiments are conducted on two general ABSA benchmark datasets of SemEval-2014. The experimental results reveal that the proposed model is of great performance and efficient for both ATE and APC tasks compared to the main baseline models.

Heng Yang

Papers

Multi-task learning model for aspect term extraction and aspect polarity classification based on dual-labels