Chapter 13 - SOMA: The Smart Social Customer Relationship Management Tool: Handling Semantic Variability of Emotion Analysis With Hybrid Technologies

Abstract

SOMA is a smart social customer relationship management tool for companies aiming to monitor and automatically deal with customers’ complaints and interactions in social networks. Emotion detection is crucial in analyzing customers’ messages and to empathize with them. Part of the project consists in highlighting, in a social network corpus, how linguistic phenomena such as negation, intentional contexts, indirect speech, rhetorical devices, and pragmatic phenomena such as genre, personal style, domain, and context have a dramatic impact on the process of detecting opinions and emotions in social networks. We describe our approach to emotion detection based on a hybrid approach combining statistics and rules. A statistical approach is used to solve classic bag-of-words problems and a symbolic rule–based approach is used to increase detection precision. Finally, we propose a new operational evaluation method grounded more in real-world problem solving than in traditional gold standard annotation approaches.