iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples
This work addresses a specific gap in quadruple extraction for sentiment analysis, focusing on implicit elements, which is an incremental advancement in the domain.
The paper tackles the problem of extracting implicit aspects, categories, opinions, and sentiments in aspect-based sentiment analysis, proposing iACOS, which significantly outperforms other methods on two benchmark datasets as measured by F1 scores.
Aspect-based sentiment analysis (ABSA) have been extensively studied, but little light has been shed on the quadruple extraction consisting of four fundamental elements: aspects, categories, opinions and sentiments, especially with implicit aspects and opinions. In this paper, we propose a new method iACOS for extracting Implicit Aspects with Categories and Opinions with Sentiments. First, iACOS appends two implicit tokens at the end of a text to capture the context-aware representation of all tokens including implicit aspects and opinions. Second, iACOS develops a sequence labeling model over the context-aware token representation to co-extract explicit and implicit aspects and opinions. Third, iACOS devises a multi-label classifier with a specialized multi-head attention for discovering aspect-opinion pairs and predicting their categories and sentiments simultaneously. Fourth, iACOS leverages informative and adaptive negative examples to jointly train the multi-label classifier and the other two classifiers on categories and sentiments by multi-task learning. Finally, the experimental results show that iACOS significantly outperforms other quadruple extraction baselines according to the F1 score on two public benchmark datasets.