Aspect extraction with enriching word representation and post-processing rules

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 timeconsuming; 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-learningbased 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.