Dense Concatenation Memory Network for Aspect Level Sentiment Analysis

Abstract

Aspect-level sentiment analysis is a fundamental task in sentiment analysis. It has many practical applications in product reviews, takeaway evaluations, and public opinion supervision. Over the past few years, previous studies have made remarkable progress. However, the existing methods still have some areas for improvement in aspect processing and feature fusion. In this study, we propose an aspectcontext dense concatenation model (ACDC) to merge deep semantic information from various aspects and contexts. ACDC first captures the interaction information between the aspect and context using an interaction matrix. Subsequently, a dense concatenation mechanism is used to extract and integrate features at different levels. In addition, we introduce two loss functions to eliminate the interference information and overfitting. On the Restaurant2014 and Laptop2014 datasets, the experimental results show that our proposed model achieves state-of-the-art performance in terms of accuracy and MF1. Furthermore, comparative experiments are conducted to verify the effectiveness of the proposed model.

RMDEASD: Integrating Rule Mining and Deep Learning for Enhanced Aspect-Based Sentiment Analysis Across Diverse Domains

“…In the comparative analysis of observed precision in converting sentences into spatial aspect-based sentiments, RMDEASD demonstrates a consistently superior performance across various sample sizes (NTS) compared to other models like DCMN [8], KGAN [19], and MAMN [41]. The precision percentages (P%) illustrate this trend clearly.…”

“…Ma and Guo [8] introduced a Dense Concatenation Memory Network for ABSA, showcasing the advancement in memory network applications for sentiment analysis. Liang et al [9] developed an embedding refinement framework targeted for ABSA, furthering the understanding of how sophisticated embedding techniques can enhance sentiment analysis accuracy.…”

“…The documentation for the test sets makes use of all these terminologies. To determine the appropriate TP, TN, FP, and FN values for these scenarios, we compared the projected Sentiment Class Instance likelihood to the actual Sentiment Class Instance status in the test dataset samples using the Dense Concatenation Memory Network (DCMN) [8], Knowledge Graph Augmented Network (KGAN) [19], and Multilevel Attention Map Network (MAMN) [41] techniques. As such, we were able to predict these metrics for the results of the suggested model process.…”

“…24k NTS (Number of Testing Sentiment Samples), RMDEASD exhibits an outstanding recall of 94.76%, significantly surpassing DCMN[8], KGAN[19], and MAMN[41]. This high recall rate at a lower sample size indicates that RMDEASD is particularly effective in identifying most of the relevant sentiments from a smaller dataset.…”

“…observed Area Under the Curve (AUC) for converting sentences into spatial aspect-based sentiments reveals essential insights into the performance of RMDEASD compared to other models like DCMN[8], KGAN[19], and MAMN[41]. AUC, a metric derived from the Receiver Operating Characteristic (ROC) curve, measures a model's ability to discriminate between classes-in this case, different sentiment classes.…”