Résumé:
Hybrid Aspect-Based Sentiment Classification (ABSC) methods make use of domain-specific, costly ontologies to make up for the lack of available aspect-level data. This paper proposes two forms of transfer learning to exploit the plenteous amount of available document data for sentiment classification. Specifically, two forms of document knowledge transfer, pretraining (PRET) and multi-task learning (MULT), are considered in various combinations to extend the state-of-the-art LCR-Rot-hop++ model. For both the SemEval 2015 and 2016 datasets, we find an improvement over the LCR-Rot-hop++ neural model. Overall, the pure MULT model performs well across both datasets. Additionally, there is an optimal amount of document knowledge that can be injected, after which the performance deteriorates due to the extra focus on the auxiliary task. We observe that with transfer learning and L1 and L2 loss regularisation, the LCR-Rot-hop++ model is able to outperform the HAABSA++ hybrid model on the (larger) SemEval 2016 dataset. Thus, we conclude that transfer learning is a feasible and computationally cheap substitute for the ontology step of hybrid ABSC models.
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