Towards using Multimodal Features of Social Networks for Improved Contextual Emotion Detection

Social networks are valuable source of information that could be used in classifying users’ emotions. In this paper, we explore the importance of certain multimodal features of social networks, other than text, that can be used in enhancing emotion detection. We study the types of posts, the degree of interaction with contacts, and the influence of contact opinions and how they tend to affect the emotions of social network users. We conducted an online survey targeting Facebook users to know how they are affected by such features. The results of our study show that status messages are the most used feature to express the social network users’ emotions, and the emotions of social network user are affected by posts and updates from friends, especially close friends. The number of likes expressed to social network users was found to positively affect their emotions. We will use such findings to prototype a system for enhanced emotion detection.