In daily conversations, people often encounter problems prompting conversational repair to enhance mutual understanding. By employing an automatic coreference solver, alongside examining repetition, we identify various linguistic features that distinguish turns when the addressee initiates repair from those when they do not. Our findings reveal distinct patterns that characterize the repair sequence and each type of repair initiation.

Humans’ emotional perception is subjective by nature, in which each individual could express different emotions regarding the same textual content. Existing datasets for emotion analysis commonly depend on a single ground truth per data sample, derived from majority voting or averaging the opinions of all annotators. In this paper, we introduce a new non-aggregated dataset, namely StudEmo, that contains 5,182 customer reviews, each annotated by 25 people with intensities of eight emotions from Plutchik’s model, extended with valence and arousal. We also propose three personalized models that use not only textual content but also the individual human perspective, providing the model with different approaches to learning human representations. The experiments were carried out as a multitask classification on two datasets: our StudEmo dataset and GoEmotions dataset, which contains 28 emotional categories. The proposed personalized methods significantly improve prediction results, especially for emotions that have low inter-annotator agreement.