TY - GEN
T1 - Emoticon recommendation in microblog using affective trajectory model
AU - Liang, Wei Bin
AU - Wang, Hsien Chang
AU - Chu, Yi An
AU - Wu, Chung Hsien
N1 - Funding Information:
Financial support: grant from the Katharine Dormandy Trust (registered charity no. 262 434) to E.H.M.
Publisher Copyright:
© 2014 Asia-Pacific Signal and Information Processing Ass.
PY - 2014/2/12
Y1 - 2014/2/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84949925452&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84949925452&partnerID=8YFLogxK
U2 - 10.1109/APSIPA.2014.7041632
DO - 10.1109/APSIPA.2014.7041632
M3 - Conference contribution
AN - SCOPUS:84949925452
T3 - 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014
BT - 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014
Y2 - 9 December 2014 through 12 December 2014
ER -