TY - GEN
T1 - A semantic graph based approach on interest extraction from user generated texts in social media
AU - Jose, Lijo M.
AU - Rahamathulla, K.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/25
Y1 - 2016/10/25
N2 - Micro-blogs and social networking websites have become a platform for self-expression. They reflect the thoughts, ideas and opinions of people on various subjects. Analyzing these texts to find the main topics mentioned in them is a fine method for targeted marketing. Targeted marketing involves identifying potential clients who might be interested in particular products or services and marketing them to these clients. Examples of targeted marketing include recommender systems and targeted advertising. Identifying the personal interests of users is a major factor that determines the quality of such systems. Commonly used techniques monitor online behavior of users like purchase histories, product views etc. or explicitly collect the user's interests through surveys and rating systems. However there have been only a few attempts to use user generated texts as a source for analyzing personal interests and preferences. This paper proposes a semantic graph based method to identify the likes and interests of users by analyzing their twitter feeds. It also put forward the design for a recommender system that can work along with the proposed interest extraction method. This method is purely based on the texts that a user leaves in a particular social network website or a micro blog. Unlike the other conventional methods there is no need to track the user activity on the Internet or conduct exclusive surveys and ratings to collect explicit ideas from the user.
AB - Micro-blogs and social networking websites have become a platform for self-expression. They reflect the thoughts, ideas and opinions of people on various subjects. Analyzing these texts to find the main topics mentioned in them is a fine method for targeted marketing. Targeted marketing involves identifying potential clients who might be interested in particular products or services and marketing them to these clients. Examples of targeted marketing include recommender systems and targeted advertising. Identifying the personal interests of users is a major factor that determines the quality of such systems. Commonly used techniques monitor online behavior of users like purchase histories, product views etc. or explicitly collect the user's interests through surveys and rating systems. However there have been only a few attempts to use user generated texts as a source for analyzing personal interests and preferences. This paper proposes a semantic graph based method to identify the likes and interests of users by analyzing their twitter feeds. It also put forward the design for a recommender system that can work along with the proposed interest extraction method. This method is purely based on the texts that a user leaves in a particular social network website or a micro blog. Unlike the other conventional methods there is no need to track the user activity on the Internet or conduct exclusive surveys and ratings to collect explicit ideas from the user.
UR - https://www.scopus.com/pages/publications/85010435005
UR - https://www.scopus.com/pages/publications/85010435005#tab=citedBy
U2 - 10.1109/SAPIENCE.2016.7684118
DO - 10.1109/SAPIENCE.2016.7684118
M3 - Conference contribution
AN - SCOPUS:85010435005
T3 - Proceedings of 2016 International Conference on Data Mining and Advanced Computing, SAPIENCE 2016
SP - 101
EP - 104
BT - Proceedings of 2016 International Conference on Data Mining and Advanced Computing, SAPIENCE 2016
A2 - R, Rajesh
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 International Conference on Data Mining and Advanced Computing, SAPIENCE 2016
Y2 - 16 March 2016 through 18 March 2016
ER -