A Lightweight Clustering-Based Approach to Discover Different Emotion Shades from Social Message Streams

With the explosion of social media, automatic analysis of sentiment and emotion from user-generated content has attracted the attention of many research areas and commercial-marketing domains, targeted at studying the social behavior of Web users and their public attitudes towards brands, social events, political actions. Capturing the emotions expressed in the written language could be crucial to support decision-making process: the emotion resulted from a tweet or a review about an item could affect the way to advertise or to trade on the Web and then make predictions about future changes in popularity or market behavior. This paper presents an experience with the emotion-based classification of textual data from a social network by using an extended version of fuzzy C-means algorithm called EFCM. The algorithm shows interesting results due to its intrinsic fuzzy nature that reflects the human feeling expressed in the text, often composed of a mix of blurred emotions, and, at the same time, the benefits of the extended version yield better classification results.