Advancements in Aspect-Based Sentiment Analysis: Leveraging Deep Learning and Machine Learning Techniques

Abstract

Aspect-based sentiment analysis (ABSA) has emerged as a pivotal task in natural language processing, focusing on extracting sentiments towards specific aspects or entities within text. Traditional approaches typically employ supervised learning techniques utilising labelled datasets. However, it presents novel methodologies to enhance the accuracy and granularity of sentiment classification. This research explores cutting-edge techniques such as deep learning architectures, transfer learning paradigms, and attention mechanisms tailored for sentiment analysis. An outline of the term is given in this article, as are the challenges, approaches, and performance of sentiment and sentiment analysis techniques based on aspects. It also presents a case study of applying these methods to analyse the online reviews and discusses the results, insights, implications, and limitations of the analysis. The new innovation is to advance an enhanced ABSA model utilising deep learning and traditional machine learning methods. It also presents LadaBERT, which is a new middle-of-the end-to-end model that is blended to solve two major problems related to memory in BERT language representation models, namely memory escalation and latency. Moreover, the Feature-Enhanced Attention CNN-BiLSTM model is developed to enhance the specificity of the presented sentiment analysis. Identifying the gaps in the current literature and evaluating the models based on various parameters such as accuracy, precision, recall, and F1-score, this provides an empirical analysis of 60 post-2019 articles. The findings underscore the effectiveness and ineffectiveness of the models in the literature and prove the applicability of the proposed plans in various applications, including social media monitoring, brand reputation management, and customer feedback analysis.