Emoji signals across Europe: Rating prediction and cross-lingual analysis

Emojis are present in user-generated content, mainly on the Internet. This fact makes emoji analysis an important aspect of information retrieval from natural language texts. This work explores the potential of emojis as indicators for rating prediction of online product reviews. As a case study, we analyze the reviews of apps on the Google Play Store. The results indicate a significant presence of emojis in the comments, particularly in extreme ratings (1 and 5 stars), consistent across languages. We conducted a quantitative evaluation of emoji-only, text-only, and text+emoji configurations using eXtreme Gradient Boosting (XGBoost) and multilingual BERT models. In 102,118 reviews from 13 European locales, the Multilingual ModernBERT (mmBERT) text+emoji model achieves MAE 0.8131 and F1-micro 0.4321, while the XGBoost text+emoji model achieves MAE 0.9749 and F1-micro 0.3702 with a shorter training time. Emoji-only models remain competitive, with XGBoost achieving MAE 0.9773 and F1-micro 0.3629, which becomes a lightweight solution for rating prediction, especially at extreme ratings. We also analyze language and country conditioning, noting stabilization and cross-lingual variability. Training efficiency per fold is reported for deployment considerations.