Emoticon recommendation in microblog using affective trajectory model

An emoticon is a metacommunicative pictorial representation which is widely used in text-based online communication such as Plurk and Facebook to convey the user's emotions. However, these social networks still lack a mechanism to provide appropriate emoticon recommendation according to the input posts. Therefore, this paper develops an approach to emoticon recommendation in microblog. Generally, a blog post is composed of at least one emotional topic. Therefore, topic tracking is the key information for emoticon recommendation. In this paper, a fixed-size window is first employed to segment a post into a number of segments. Then, these segments are projected to emoticon profiles in the emoticon space through latent Dirichlet allocation (LDA). An affective trajectory model characterizing the emoticon profiles of the segment sequence is proposed to construct a recommendation model based on k-medoids algorithm. Finally, emoticon recommendation can be realized by similarity measure based on Hausdorff distance. To evaluate the performance of our proposed approach, the experimental data were crawled from Plurk for training and evaluation. The results show the effectiveness of the proposed approach.