| Summary: | The proliferation of user-generated content on social networks and websites has heightened the significance of sentiment analysis, also known as opinion mining, as a critical tool for comprehending people’s attitudes toward various topics. Aspect-level sentiment analysis, which considers specific aspects or features of texts, provides a more comprehensive view of sentiment analysis. The aspect-level approach encompasses both explicit and implicit aspects, where explicit aspects are readily mentioned in texts while implicit aspects are implied or inferred from contextual clues. Despite the significance of implicit aspects in the overall review, previous research has predominantly focused on explicit aspect extraction. Limited attention has been given to the extraction of implicit aspects, despite their potential impact on capturing the complete sentiment picture of texts. Therefore, this study aims to find an aspect extraction solution capable of identifying and extracting both explicit and implicit aspects from texts. This study compares various machine and deep learning models on the SemEval-2014 and SemEval-2016 restaurant datasets. The experimental analysis demonstrates that the proposed Aspect-BiLSTM model emerged as the best-performing model, achieving high accuracy in classifying both explicit and implicit aspects, with 92.9% accuracy for the 2014 and 90.7% accuracy for the 2016 datasets. Notably, the proposed solution was able to capture multiple aspects of texts, making it more robust and versatile. This study highlights the efficacy of the Aspect-BiLSTM model for aspect extraction, which will give valuable insights into the advancement of aspect-level sentiment analysis.
Doi: 10.28991/ESJ-2024-08-01-05
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