Aspect-based sentiment analysis (ABSA) aims at automatically inferring the specific sentiment polarities toward certain aspects of products or services behind the social media texts or reviews, which has been a fundamental application to the real-world society. Since the early 2010s, ABSA has achieved extraordinarily high accuracy with various deep neural models. However, existing ABSA models with strong in-house performances may fail to generalize to some challenging cases where the contexts are variable, i.e., low robustness to real-world environments. In this study, we propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training. First, we strengthen the current best-robust syntax-aware models by further incorporating the rich external syntactic dependencies and the labels with aspect simultaneously with a universal-syntax graph convolutional network. In the corpus perspective, we propose to automatically induce high-quality synthetic training data with various types, allowing models to learn sufficient inductive bias for better robustness. Last, we based on the rich pseudo data perform adversarial training to enhance the resistance to the context perturbation and meanwhile employ contrastive learning to reinforce the representations of instances with contrastive sentiments. Extensive robustness evaluations are conducted. The results demonstrate that our enhanced syntax-aware model achieves better robustness performances than all the state-of-the-art baselines. By additionally incorporating our synthetic corpus, the robust testing results are pushed with around 10% accuracy, which are then further improved by installing the advanced training strategies. In-depth analyses are presented for revealing the factors influencing the ABSA robustness.

Joint aspect category sentiment analysis (ACSA) and rating prediction (RP) is a newly proposed task (namely ASAP) that integrates the characteristics of both fine-grained and coarse-grained sentiment analysis. However, the prior joint models for the ASAP task only consider the shallow interaction between the two granularities. In this work, we gain the inspiration from human intuition, presenting an innovative from-fine-to-coarse reasoning framework for better joint task performance. Our system advances mainly in three aspects. First, we additionally make use of the category label text features, co-encoding them with the input document texts, allowing to accurately capture the key clues of each category. Second, we build a fine-to-coarse hierarchical label graph, modeling the aspect categories and the overall rating as a hierarchical structure for full interaction of the two granularities. Third, we propose to perform global iterative reasoning with a cross-collaboration between the hierarchical label graph and the context graphs, enabling sufficient communication between categories and review contexts. Based on the ASAP dataset, experimental results demonstrate that our proposed framework outperforms state-of-the-art baselines by large margins. Further in-depth analyses prove that our method is effective on addressing both the unbalanced data distribution and the long-text issue.

Existing works for document-level sentiment classification task treat the review document as an overall text unit, performing feature extraction with various sophisticated model architectures. In this paper, we draw inspiration from fine-grained sentiment analysis, proposing to first learn the latent target-opinion distribution behind the documents, and then leverage such fine-grained prior knowledge into the classification process. We model the latent target-opinion distribution as hierarchical variables, where global-level variable captures the overall target and opinion, and local-level variables retrieve the detailed opinion clues at the word level. The proposed method consists of two main parts: a variational module and a classification module. We employ the conditional variational autoencoder to make reconstructions of the document, during which the user and product information can be integrated. In the classification module, we build a hierarchical model based on Transformer encoders, where the local-level and global-level prior distribution representations induced from the variational module are injected into the word-level and sentence-level Transformers, respectively. Experimental results on benchmark datasets show that the proposed method significantly outperforms strong baselines, achieving the state-of-the-art performance. Further analysis shows that our model is capable of capturing the latent fine-grained target and opinion prior information, which is highly effective for improving the task performance.