Sentiment Analysis for Requirements Elicitation from App Reviews: A Systematic Mapping Study

In software stores, user reviews are the most direct feedback on product experiences and requirements. Sentiment analysis (SA) technology has been proven to be efficient for review mining-based requirements elicitation. Our objective is to identify the trend and actuality in SA for requirements elicitation from app reviews. We conduct a systematic mapping study, retrieving 740 citations from 2013 to 2023, and 33 articles are retained as primary studies. The overall research posture is increasing yearly. There are 24 self-crawled and seven specially created datasets. SVM is the most commonly used classifier, while BERT represents the most innovative algorithm with a good performance. Precision, recall, and F-measure are the most popular metrics. An SA framework based on feature granularity is proposed to guide newcomers in this field. It is proven that SA can effectively assist in transitioning from traditional methods to review mining in requirements elicitation. Overall, SA for requirements elicitation from app reviews is becoming an increasingly mature cross-research field. Future research can focus on building highly generalized and multilingual datasets, using more fine-grained feature extraction methods to improve requirements elicitation accuracy, and exploring BERT families or other attention-based deep learning models.