ML-Ask - eMotive eLement and Expression Analysis

ML-Ask, or eMotive eLement and Expression Analysis system, is a keyword-based language-dependent system for automatic affect annotation on utterances in Japanese. It uses a two-step procedure: 1. Specifying whether a sentence is emotive, and 2. Recognizing particular emotion types in utterances described as emotive. The database of emotemes was hand-crafted and contains 907 emotemes, which include such groups of emotemes as interjections, mimetic expressions (gitaigo in Japanese), vulgar language, or emotive sentence markers. The emotive expression database is a collection of over two thousand expressions describing emotional states. ML-Ask also implements the idea of Contextual Valence Shifters (CVS) for Japanese with 108 syntactic negation structures. Finally, ML-Ask implements Russell’s two dimensional model of affect. The model assumes that all emotions can be represented in two dimensions: the valence (positive/negative) and activation (activated/deactivated)