HYBRID DEEP LEARNING TECHNIQUES FOR ENHANCED ASPECT BASED SENTIMENT ANALYSIS AND CONTEXTUAL FEATURE OPTIMIZATION

Abstract

The exponential growth of user-generated text data on social media highlights the need for effective sentiment analysis systems that can be scaled to support large textual inputs with high contextual accuracy. In many traditional approaches, problems associated with noisy data, unsound feature selection, and lack of scalability prevail, thereby leading to a lack of effective sentiment classification solutions. This study introduces a new framework of aspect-based sentiment analysis (ABSA), which incorporates the best advanced techniques of preprocessing, feature extraction, feature selection, and classification to achieve unmatched performance. The methodology involves robust preprocessing, including tokenization, lexical normalization, and punctuation removal, to ensure a clean input for further processing. Feature extraction is performed using pretrained embeddings, such as robust optimized bidirectional encoder representations from transformers (RoBERTa) and Global Vectors for Word Representation (GloVe), capturing both contextual and word-level relationships. Feature selection employs a hybrid Arithmetic Optimization Algorithm (AOA) and Henry Gas Solubility Optimization (HGS) refined by hierarchical attention mechanisms to retain relevant features while reducing dimensionality. The classification phase utilizes an ((ARGCN), which integrates attention mechanisms and capsule networks to provide refined sentiment predictions. The experimental findings on Sentiment Analysis dataset 1 show 99.85% accuracy, 99.80% precision, 99.88% recall, 99.83% F1 score, and 99.90% specificity. The same was obtained for Dataset 2, with the metrics lying remarkably higher, with an accuracy of 99.89%, precision of 99.87%, recall of 99.90%, F1 score of 99.88%, and specificity of 99.92%. These results depict the robustness and scalability of the proposed system while indicating a greatly improved state-of-the-art method, proving its superiority in setting a new benchmark for sentiment classification tasks.