@article { author = {Jaderyan, Morteza and Khotanlou, Hassan}, title = {Automatic Hashtag Recommendation in Social Networking and Microblogging Platforms Using a Knowledge-Intensive Content-based Approach}, journal = {International Journal of Engineering}, volume = {32}, number = {8}, pages = {1101-1116}, year = {2019}, publisher = {Materials and Energy Research Center}, issn = {1025-2495}, eissn = {1735-9244}, doi = {10.5829/ije.2019.32.08b.06}, abstract = {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.}, keywords = {Content enrichment,Hashtag Recommendation,Knowledge-Intensive,ontology,semantic network representation,Structured Knowledge base}, url = {https://www.ije.ir/article_89994.html}, eprint = {https://www.ije.ir/article_89994_65851a3cb68cfc1e6e0519244a668820.pdf} }