Logic tensor network with massive learned knowledge for aspect-based sentiment analysis

Aspect-based sentiment analysis assists service providers to better understand users' opinions expressed in massive amounts of online posts, because it automatically infers users' sentiments towards the aspect terms of interest. Recently, several researchers have attempted to apply first-order logic (FOL) rules to deep neural networks via the posterior constraint method. However, existing methods simply apply a priori constraints to represent the FOL with coefficients selected by hand, which requires improvements in incorporating and adapting abstract knowledge in data. In this study, we propose a novel logic tensor network with massive rules (LTNMR) for aspect-based sentiment analysis, which is constructed by incorporating FOL. Specifically, we integrate two types of knowledge into the logic tensor network: (1) dependency knowledge, which improves the efficiency of the capture of aspect-related words and (2) the human-defined knowledge rule, which helps the classifier understand the sentiment of the extracted aspect-related words. Furthermore, to achieve high inferring accuracy, we propose a mutual distillation structure knowledge injection (MDSKI) strategy. MDSKI transfers dependency knowledge from teacher Bert to LTNMR, which acts as the student network. Experiments demonstrate that the proposed LTNMR, combined with the MDSKI strategy, substantially outperforms state-of-the-art results for aspect-based sentiment analysis. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Aspect-based sentiment analysis helps service providers understand users' opinions in online posts, and the proposed logic tensor network with massive rules significantly improves the accuracy of the analysis.