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. T
he 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.
ActiveAnno is an annotation tool focused on document-level annotation tasks developed both for industry and research settings. It is designed to be a general-purpose tool with a wide variety of use cases. It features a modern and responsive web UI fo
r creating annotation projects, conducting annotations, adjudicating disagreements, and analyzing annotation results. ActiveAnno embeds a highly configurable and interactive user interface. The tool also integrates a RESTful API that enables integration into other software systems, including an API for machine learning integration. ActiveAnno is built with extensible design and easy deployment in mind, all to enable users to perform annotation tasks with high efficiency and high-quality annotation results.
