%0 Journal Article %T Automatic Hashtag Recommendation in Social Networking and Microblogging Platforms Using a Knowledge-Intensive Content-based Approach %J International Journal of Engineering %I Materials and Energy Research Center %Z 1025-2495 %A Jaderyan, Morteza %A Khotanlou, Hassan %D 2019 %\ 08/01/2019 %V 32 %N 8 %P 1101-1116 %! Automatic Hashtag Recommendation in Social Networking and Microblogging Platforms Using a Knowledge-Intensive Content-based Approach %K Content enrichment %K Hashtag Recommendation %K Knowledge-Intensive %K ontology %K semantic network representation %K Structured Knowledge base %R 10.5829/ije.2019.32.08b.06 %X 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. %U https://www.ije.ir/article_89994_65851a3cb68cfc1e6e0519244a668820.pdf