Emotion Guided & Aspect Aware Sentiment Classification using Prototype Memory and Confidence Filtering on User Reviews
TL;DR
A new Emotion-Guided Aspect-Aware Sentiment Classification System which can effectively and reliably classify user reviews is proposed which integrates the following processes: contextual preprocessing, aspect extraction, fine-grained emotion detection, memory of emotion prototype, retrieval-enhanced reasoning, emotion-sensitive feature fusion, sentiment classification and confidence-based filtering, into one architecture.
Abstract
Sentiment analysis of customer reviews is an important tool in getting to know customer opinion, quality of service and product feedback. Nevertheless, classic approaches tend to label a whole review set of coarse polarity categories, which does not reflect aspect-based opinions and latent emotional trends. In order to address this deficiency, this paper proposes a new Emotion-Guided Aspect-Aware Sentiment Classification System which can effectively and reliably classify user reviews. The proposed architecture integrates the following processes: contextual preprocessing, aspect extraction, fine-grained emotion detection, memory of emotion prototype, retrieval-enhanced reasoning, emotion-sensitive feature fusion, sentiment classification and confidence-based filtering, into one architecture. The system can identify the key aspects of quality, delivery, as well as support and then emotions such as joy, anger, trust, frustration, sadness, and disappointment. These affective signals has employed in order to enhance the sentiment forecasting. Experimental data show that the proposed model reaches 93.40 percent accuracy, 92.85 percent precision, 92.30 percent recall, and 92.57 percent F1-score that are better than the base models like SVM, LSTM and BERT. The ablation experiment also verified that all the elements associated with aspect extraction, emotion detection, prototype memory, retrieval module and confidence filtering are all factors contributing to the boost in performance.