<![CDATA[User reviews are a valuable source of feedback for software developers, as they contain user requirements, opinions, and expectations regarding app usage, including dislikes, feature requests, and reporting bugs. However, extracting and analyzing user requirements from user reviews is ineffective due to the large volume, unstructured nature, and varying quality of the reviews. Therefore, further research is not just necessary but crucial to effectively explore methods to gather informative and meaningful user feedback. This study aims to investigate, analyze, and summarize the methods of requirement classification and prioritization techniques derived from user reviews. This review revealed that leveraging opinion mining, sentiment analysis, natural language processing, or any stacking technique can significantly enhance the extraction and classification processes. Additionally, an updated matrix taxonomy has been developed based on a combination of definitions from various studies to classify user reviews into four main categories: information seeking, feature request, problem discovery, and information giving. Furthermore, we identified Naive Bayes, SVM, and Neural Networks algorithms as dependable and suitable for requirement classification and prioritization tasks. The study also introduced a new 4-tuple pattern for efficient requirement prioritization, which included elicitation technique, requirement classification, additional factors, and higher range priority value. This study highlights the need for better tools to handle complex user reviews. Investigating the potential of emerging machine learning models and algorithms to improve classification and prioritization accuracy is crucial. Additionally, further research should explore automated classification to enhance efficiency.]]>
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