Aspect Term Extraction and Hierarchical Kronecker Deep Learning Network for Sentiment Classification

The process of natural language processing (NLP) to classify and identify sentiments as negative, positive, or neutral automatically is called sentiment classification. Sentiment Analysis improves the service and product based on customer feedback. Sentiment Analysis is beneficial for business operations for analyzing customer feedback to improve customer satisfaction and product development. However, there are some limitations, such as overfitting issues, flexibility, interpretability, generalization, stability, low energy efficiency, and complexity. The traditional methods do not capture the contextual semantics of the sentence, which leads to poor sentiment classification in the presence of sarcasm, irony, or idiomatic expressions. Also, many of the methods ignore the textural cues like hashtags, all-caps words, punctuation, and negations. To overcome above-mentioned issues, an efficient hierarchical Kronecker deep learning network (HKDL-Net) is established. Here, HKDL-Net is developed by the combination of hierarchical deep learning for text classification (HDLTex) and deep Kronecker network (DKN). Initially, reviews are subjected to bidirectional encoder representation from transformers (BERT) tokenization to split text into specific words. Then, aspect term extraction (ATE) is performed. Afterwards, feature extraction is executed by using extracted aspect terms for mining features, such as emotion, hashtag, all-caps, number of sentences in review, elongated units, numerical words, a bag of units, question marks, number of words in a review, and negation. The sentiment classification is ultimately achieved through the proposed HKDL-Net. Moreover, HKDL-Net attained better results in sentiment classification with a precision of 91.877%, recall of 93.899%, F1-score of 92.877%, and accuracy of 97.568%, respectively. This shows that the devised model effectively detects the non-standard language and enhances the classification performance of sentiments in subtle or complex cases. Also, it can capture contextual relationships between words, which helps to improve its ability to understand sarcasm, irony, or idiomatic expressions.