Aspect extraction with enriching word representation and post-processing rules

Abstract

The detection of mentioned aspects in product reviews is one of the significant and complex tasks in opinion mining. Recently, contextual-based approaches have significantly improved the accuracy of aspect extraction over non-contextual embeddings. However, these approaches are often computationally expensive and time-consuming; thus, applying such heavy models with insufficient resources and within runtime systems is impractical in many realistic scenarios. The present investigation sought an efficient, practical deep-learning-based model that relies on the complementary power of various existing non-contextual embeddings. In this regard, two morphology-based (character and FastText) and two syntax-based (POS and extended dependency skip-gram) embeddings were used alongside a base word embedding (GloVe) to form an enriched word representation layer. The presented model was integrated into the proposed network architecture (extended BiGRU). Finally, two novel post-processing rules were applied to refine the errors in the model's predictions. The proposed model achieved F-scores of 0.86, 0.91, 0.79, and 0.80 for the SemEval 2014 laptop domain and the SemEval 2015–2016 restaurant domain, respectively. Furthermore, the results were validated by comparing the computational and temporal efficiency of the proposed model with seven BERT-family transformers through statistical tests.