关键词: 属性级情感分析, 细粒度情感信息抽取, 对话文本, 词对关系建模

Abstract: Aspect-based sentiment analysis aims to capture fine-grained sentiment information contained in text and has drawn considerable attention due to its wide applications. However, traditional research in aspect-based sentiment analysis predominantly relies on non-interactive review texts, with limited investigation into aspect-based sentiment analysis within interactive dialogue contexts. Addressing this gap, this paper proposes a joint extraction task for aspect-based sentiment information in interactive dialogue scenarios. The task aims to extract complete fine-grained sentiment information triplets consisting of target aspects, opinion expressions, and corresponding sentiment polarities, thereby obtaining comprehensive sentiment information from the final utterance in an interactive dialogue. To this end, this paper devises an end-to-end extraction method based on word-pair relation modeling, where in the relationship between word pairs are modeled to map dialogue text onto a directed graph, transforming the decoding process into a search for specific cyclic structures within the graph. To enhance the accuracy of word-pair relationship modeling, this paper introduces a novel model architecture that integrates relative distance information and dialogue turn information when constructing word-pair relationship representations, and utilizes multi-granularity 2D convolution to enhance interaction between word pairs. Additionally, this paper proposes a dynamic loss weighting method to effectively mitigate the issue of imbalanced category distributions in word-pair relation within dialogue texts. Experimental results demonstrate that, the proposed method outperforms strong baseline methods, achieving an average F1 score improvement of 7.70% and a maximum improvement of 15.05%.

Key words: aspect-based sentiment analysis, fine-grained sentiment information extraction, dialogue text, word-pair relationship modeling