EIAASG: Emotional Intensive Adaptive Aspect-Specific GCN for sentiment classification

Deep learning techniques and attention schemes are used by many researchers for classifying the sentiments. Retrieval of semantic relationship between the aspects with words of context will improve the classification accuracy. This was done by Aspect-Specific Graph Convolutional Networks (ASGCN) which utilises aspect-specific relationships and attention scheme by researchers. Long-range dependencies and sensitive-important words are missing in these methods. This work proposes two novel approaches for improving the effectiveness of sentiment classification. First, we propose a method, Adaptive Aspect-Specific GCN (AASGCN) for enhancing ASGCN by incorporating adaptive weights into ASGCN to better capturing of the semantic meaning of the opinion target. Second, we introduce an Emotional Intensive Sentiment Reasoning (EISR) that incorporates emotional intensive information into the mechanism. We experiment our proposed work along with many existing work's datasets such as LAP14 (Pontiki et al., 2014), TWITTER (Dong et al., 2014), REST14 (Pontiki et al., 2014), REST15 (Pontiki et al., 2015), and REST16 (Pontiki et al., 2016). The results prove that AASGCN performs well than the range of state-of-the-art models and can be substantially improved by incorporating the two approaches.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This study proposes two novel approaches for improving the effectiveness of sentiment classification. The first approach incorporates adaptive weights into Aspect-Specific GCN (AASGCN) to better capture the semantic meaning of the opinion target. The second approach introduces emotional intensive information into sentiment reasoning. Experimental results show that AASGCN outperforms state-of-the-art models and can be substantially improved by incorporating these two approaches.