Leveraging Text Generation Models for Aspect-Based Sentiment Text Generation

Abstract

Sentiment analysis is a vital tool in natural language processing (NLP), enabling the interpretation and understanding of opinions expressed in textual data. Traditional sentiment analysis methods, often limited to document or sentence-level analysis, primarily focus on identifying the sentiment without generating detailed sentiment text expressions. To address this limitation, we propose a novel Aspect-Specific Sentiment Expression Generation (ASSEG) model. Unlike traditional approaches, the ASSEG model leverages advanced text generation models, such as GPT-2 and T5, to automatically generate sentiment expressions tailored to diverse aspects of entities discussed in the text. The key innovation of our approach lies in the integration of aspect-specific attention mechanisms, which enable the model to effectively identify and prioritize aspects within the text, generating coherent and contextually relevant sentiment expressions. Our methodology includes using Recurrent Generative Adversarial Networks (RGANs) for data augmentation, addressing data imbalance issues, and enhancing the robustness of sentiment analysis models. Experimental evaluations were conducted on domain-specific datasets, including laptop and restaurant reviews. Our experimental evaluations on domain-specific datasets, including laptop and restaurant reviews, demonstrate the superior performance of our ASSEG model. The GPT-2 model achieved an accuracy of 75% and 65%, and an F1 score of 77% and 65% for restaurant and laptop datasets, respectively. Meanwhile, the T5 model outperformed GPT-2, achieving an accuracy of 85% and 75%, and an F1 score of 83% and 74% for restaurant and laptop datasets, respectively. These results highlight the potential of the ASSEG model, offering deeper insights into user opinions by generating detailed and contextually relevant sentiment expressions.