Sentiment-Aware Summary Generation for User Reviews Using Deep Learning Models
DOI:
https://doi.org/10.21015/vtse.v13i3.2240Abstract
User reviews are a valuable source of information for potential buyers. User reviews provide valuable feedback that reflects genuine customer experiences, enabling future buyers to make more informed decisions. Reviewing every single comment can be a challenging, lengthy, and overwhelming process. Text summarization helps by providing concise summaries, allowing users to quickly grasp key points from multiple reviews. Additionally, sentiment analysis extracts subjective information, such as overall opinions, strengths, weaknesses, and recommendations, helping potential buyers in making informed decisions. This study proposes the XLNet model for sentiment analysis of user reviews and LED (Longformer Encoder-Decoder) for text summarization of user reviews. XLNet is a pre-trained transformer model that captures bidirectional relationships between words to improve sentiment analysis, while LED is a transformer-based model designed for efficient text summarization of long documents by using a sparse attention mechanism. The results show that XLNet achieves high accuracy in sentiment classification on the Amazon Fine Food Reviews dataset of 50K reviews, while LED generates effective and concise summaries, achieving the highest ROUGE and METEOR scores among the tested summarization models. To ensure robust evaluation, multiple metrics, including ROUGE, METEOR, MoverScore, and BERTScore-F1, were applied across models, providing both lexical and semantic perspectives on summarization quality. By performing both sentiment analysis and text summarization within a single framework, this approach efficiently extracts meaningful insights from large datasets, streamlining the decision-making process for users.
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