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Knowledge and Information Systems

09.05.2024 | Review

Trends and challenges in sentiment summarization: a systematic review of aspect extraction techniques

verfasst von: Nur Hayatin, Suraya Alias, Lai Po Hung

Erschienen in: Knowledge and Information Systems

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Abstract

Sentiment Summarization is an automated technology that extracts important features of sentences and then reorganizes selected words or sentences by their aspect class and sentiment polarity. This emerging research area wields considerable influence, where a sentiment-based summary can provide insight into users’ subjective opinions, creating social engagement that benefits industry players and entrepreneurs. Meanwhile, systematic studies examining sentiment-based summarization, particularly those delving into aspect levels, are still limited. Whereas aspects are crucial to obtain a comprehensive assessment of a product or service for improving sentiment summarization results. Hence, we conducted a comprehensive survey of aspect extraction techniques in sentiment summarization by classifying techniques based on sentiment analysis levels and features. This work analyzes the current research trends and challenges in the research domain from a different perspective. More than 150 literature published from 2004 to 2023 are collected mainly from credible academic databases. We summarized and performed a comparative analysis of the sentiment summarization approaches and tabulated their performance based on different domains, sentiment levels, and features. We also derived a thematic taxonomy of aspect extraction techniques in sentiment summarization from the analysis and illustrated its usage in various applications. Finally, this study presents recommendations for the challenges and opportunities for future research development.

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Metadaten
Titel
Trends and challenges in sentiment summarization: a systematic review of aspect extraction techniques
verfasst von
Nur Hayatin
Suraya Alias
Lai Po Hung
Publikationsdatum
09.05.2024
Verlag
Springer London
Erschienen in
Knowledge and Information Systems
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-024-02075-w

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