A Novel Hybrid Deep Learning Model for Aspect-Based Sentiment Analysis (ABSA)

A smooth sentiment analysis task, called Aspect-Based Sentiment Analysis (ABSA), focuses on pinpointing specific aspect phrases in a review and determining the sentiment polarity associated with each one. Unlike traditional sentiment analysis, which only captures the overall opinion expressed in a text, ABSA aims to extract detailed insights by evaluating sentiments tied to distinct components of a product, service, or topic. ABSA involves gathering and analysing public opinions, feelings, and attitudes, providing valuable information for businesses and organizations looking to improve customer satisfaction and decision-making processes. One major difficulty lies in the presence of irrelevant or noisy information in the review text, which can obscure the true sentiment. Additionally, accurately detecting the boundaries of aspect terms is complex, as these terms often appear in various forms and contexts. Another challenge is the subtle and implicit nature of sentiment expression, which can be further complicated by the use of negations, sarcasm, and rhetorical devices that alter the intended sentiment. The suggested solution uses a hybrid model that gives sentiment polarity to both the individual aspect phrases and the sentence as a whole to overcome these difficulties. It combines two cutting-edge neural architectures: a Long Short-Term Memory (LSTM) and a Graph Attention Network (GAT). LSTM networks are well-suited for determining the overall sentiment of a sentence due to their strength in modelling the sequential structure of textual data. In the meantime, the GAT is quite good at simulating the connections between words in a sentence, which enables it to draw attention to the pertinent context of each aspect term. The proposed system achieves an accuracy of 94.22% on the laptop dataset, and its results are compared with baselines as well as existing works on ABSA. The comparison shows that the performance of the proposed approach is better than the existing approaches in the literature.