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<p>In the field of Natural Language Processing (NLP), Aspect-Based Sentiment Analysis (ABSA) is currently a trending area of research. Gathering people’s opinions and exchanging information has always been common practice. With the prevalence of internet-connected devices, individuals can easily share their thoughts and real-time updates. As digital data grows, it can be leveraged to analyze people’s sentiments. Over the last decade, extensive research has been dedicated to sentiment analysis. Aspect extraction has become crucial for effectively categorizing sentiments through Sentiment Analysis (SA). This paper comprehensively reviews aspect-based Sentiment Analysis (ABSA) using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology. The study encompasses various implicit and explicit studies and their respective limitations and findings. Additionally, it delves into describing ABSA and its core tasks while also highlighting future directions such as aspect extraction, implicit aspect extraction, fake &amp; sarcasm detection, opinion spam detection, handling grammatical errors, hidden emotion extraction, implicit language detection, double implicit, and spam &amp; fake reviews. The study observes that ABSA tasks often utilize supervised learning and hybrid techniques. Ultimately, the review aims to inspire innovative researchers by providing a comprehensive overview of the field, benefiting both novices and seasoned researchers by helping them better understand implicit and explicit aspect extraction.</p>

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PRISMA-based approach to study implicit & explicit aspect-based sentiment analysis

  • 期刊论文

  • 发表日期：

    2025年6月4日

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  • Amit Chauhan,
  • Rajni Mohana

摘要

In the field of Natural Language Processing (NLP), Aspect-Based Sentiment Analysis (ABSA) is currently a trending area of research. Gathering people’s opinions and exchanging information has always been common practice. With the prevalence of internet-connected devices, individuals can easily share their thoughts and real-time updates. As digital data grows, it can be leveraged to analyze people’s sentiments. Over the last decade, extensive research has been dedicated to sentiment analysis. Aspect extraction has become crucial for effectively categorizing sentiments through Sentiment Analysis (SA). This paper comprehensively reviews aspect-based Sentiment Analysis (ABSA) using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology. The study encompasses various implicit and explicit studies and their respective limitations and findings. Additionally, it delves into describing ABSA and its core tasks while also highlighting future directions such as aspect extraction, implicit aspect extraction, fake & sarcasm detection, opinion spam detection, handling grammatical errors, hidden emotion extraction, implicit language detection, double implicit, and spam & fake reviews. The study observes that ABSA tasks often utilize supervised learning and hybrid techniques. Ultimately, the review aims to inspire innovative researchers by providing a comprehensive overview of the field, benefiting both novices and seasoned researchers by helping them better understand implicit and explicit aspect extraction.