Improving the Extraction of Aspect Terms from Customer Reviews using MSRL-NET and BERT

Aspect Term Extraction (ATE) is a significant task in natural language processing that determinations to normalize and extract particular aspects or properties of things referenced in text. This technique is essential in a dissimilarity of applications, with sentiment analysis, opinion mining, and review summarization. The determination of aspect term extraction is to automatically label and classify words or phrases that express aspects of a specific entity, which could be products, services, or any topic under discussion. In ATE, static word embeddings are required to capture dynamic word meanings properly. We designate a original method for dealing with the intricacies of this difficult and enhancing sentiment analysis accuracy, which uses a Multi-level Semantic Relation-enhanced Learning Network (MSRL-Net) and Bidirectional Encoder Representations from Transformers (BERT). In this paper, we suggest a synergistic framework that uses MSRL-Nets to develop the resolution and clarity of text representations, subsequent by BERT's input format, which is limited to a collection of words and cannot include more context knowledge. MSRL-Net and BERT addresses aspect extraction, sentiment polarity identification, and context-dependent sentiment comprehension by leveraging MSRL-Net's capabilities to create high-resolution text representation and BERT's contextualized language considerate. The combination of MSRL-Nets with BERT designates a promising future path for aspect-based sentiment analysis, with applications such as customer feedback analysis, market research, and social media sentiment monitoring. The experimental evaluation revealed that the proposed technique outperforms the SemEval dataset regarding precision, F1 score, and accuracy.