SenticNet and Abstract Meaning Representation driven Attention-Gate semantic framework for aspect sentiment triplet extraction

Abstract

Aspect sentiment triplet extraction aims to analyze aspect-level sentiment in the form of triplets, including extracting aspect-opinion pairs and predicting the sentiment polarities of these pairs. Many recent works rely on syntactic information (e.g. part-of-speech and syntactic dependency relation) to handle this semantic task, which ignores uncommon part-of-speech items and matches semantically unrelated words. To overcome these drawbacks, we propose a SenticNet and Abstract Meaning Representation (AMR) driven Attention-Gate semantic framework (SAAG), which introduces semantic sentiment knowledge SenticNet and semantic structure AMR as semantic information to replace syntactic information. To highlight the affective meanings in words, an affective-driven attention mechanism is designed to emphasizes sentiment intent within word representations. To match semantically related words, the designed AMR-driven gate mechanism balances the word pair expressions under varying semantic contexts. Extensive experiments on two public datasets demonstrate the effectiveness of our approach.