TY - JOUR ID - 89994 TI - Automatic Hashtag Recommendation in Social Networking and Microblogging Platforms Using a Knowledge-Intensive Content-based Approach JO - International Journal of Engineering JA - IJE LA - en SN - 1025-2495 AU - Jaderyan, Morteza AU - Khotanlou, Hassan AD - Department of Computer Engineering, Bu Ali Sina University, Hamedan, Iran AD - Department of Computer Engineering, Bu-Ali Sina University, Hamedan, Iran Y1 - 2019 PY - 2019 VL - 32 IS - 8 SP - 1101 EP - 1116 KW - Content enrichment KW - Hashtag Recommendation KW - Knowledge-Intensive KW - ontology KW - semantic network representation KW - Structured Knowledge base DO - 10.5829/ije.2019.32.08b.06 N2 - In social networking/microblogging environments, #tag is often used for categorizing messages and marking their key points. Also, since some social networks such as twitter apply restrictions on the number of characters in messages, #tags can serve as a useful tool for helping users express their messages. In this paper, a new knowledge-intensive content-based #tag recommendation system is introduced. The proposed system works by integrating structured knowledge in every core component. First, the relevant features, semantic structures and information-content are extracted from messages. Since little information can often be placed in a message, a content enrichment module is introduced to identify information structures that can improve the representation of message. The extracted features are represented by semantic network. Then, a hybrid and multi-layered similarity module identifies the commonalities and differences of the features, semantics and information-content in messages. At the end, #tags are recommended to users based on #tags in contextually similar messages. The system is evaluated on Tweets2011 dataset. The results suggests that the proposed method can recommend suitable #tags in negligible operational time and when little content is available. UR - https://www.ije.ir/article_89994.html L1 - https://www.ije.ir/article_89994_65851a3cb68cfc1e6e0519244a668820.pdf ER -