Improving Sentiment Analysis of Mobile App Reviews through Emoji and Slang Normalization

Abstract

Mobile app reviews provide valuable feedback for developers and policymakers, but their informal and noisy nature poses significant challenges for sentiment analysis systems. In particular, the frequent use of emojis and slang expressions can distort the semantic interpretation of user opinions and negatively impact classification performance. This paper investigates the effect of emoji and slang normalization on BERT-based sentiment analysis of mobile app reviews. Using a dataset of Instagram and WhatsApp reviews labeled through star ratings, we compare a baseline BERT model with enhanced variants incorporating emoji normalization, slang normalization, and their combination. Experimental results show that normalization improves overall classification performance, achieving an increase of approximately 3-4 percentage points in macro F1-score compared to the baseline. The findings suggest that handling informal digital expressions reduces noise and improves model robustness when analyzing user-generated content. This study highlights the importance of preprocessing strategies for more reliable sentiment analysis in real-world app review environments and provides insights for developing more inclusive and adaptable text analytics systems.