This talk presents the Character Mining project that extracts and infers various information about individual characters in multiparty dialogue. The long-term goal of this project is to develop a machine comprehension system that understands human conversations. Currently, this project focuses on three tasks, character identification, emotion detection, and reading comprehension. Character identification is an entity linking task that identifies each mention referring to a human (e.g., she, mom) as a certain character in the dialogue. We introduce the agglomerative convolutional neural network model that gives the F1 score of 86.76% for this task. Emotion detection classifies each utterance in the dialogues to one of seven emotions (sad, mad, scared, powerful, peaceful, joyful, and neutral). We introduce the sequence-based convolutional neural network model that shows the accuracies of 37.51% and 53.76% for fine and coarse-grained emotions, respectively. Finally, reading comprehension is challenged, where the input documents are dialogues, and the queries are sentences describing events in these dialogues. We suggest several attention-based deep neural models showing accuracies over 72% and robust performance for long documents.