We present a neural-network-driven model for annotating frustration intensity in customer support tweets, based on representing tweet texts using a bag-of-words encoding after processing with subword segmentation together with non-lexical features. The model was evaluated on tweets in English and Latvian languages, focusing on aspects beyond the pure bag-of-words representations used in previous research. The experimental results show that the model can be successfully applied for texts in a non-English language, and that adding non-lexical features to tweet representations significantly improves performance, while subword segmentation has a moderate but positive effect on model accuracy. Our code and training data are publicly available.