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Journals / Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi / 2020 / Cilt: 28 Sayı: 3

INVESTIGATION OF TEXT AND IMAGE BASED SEMANTIC INFERENCE METHODS IN SOCIAL NETWORKS

SOSYAL AĞLARDA METİN VE GÖRÜNTÜ TABANLI ANLAMSAL ÇIKARIM YÖNTEMLERİNİN İNCELENMESİ

Sümeyye BAYRAKDAR
İbrahim YÜCEDAĞ
Pages
283–288
Publication Date
2020-12-31
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Finally, information was given on what can be done in this area in the future.

📌 3 Key Takeaways

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Objective & Hypothesis:Due to the fact that social networks are becoming more and more popular, many users are continuously active social networking users.
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Key Findings & Data:In this way, there is a huge amount of data generated by users on social networks.
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Conclusion & Impact:Finally, information was given on what can be done in this area in the future.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Analiz · Anlamsal çıkarım · Sosyal Ağ

Abstract

Due to the fact that social networks are becoming more and more popular, many users are continuously active social networking users. Active users are constantly sharing what they see and think. Social network users follow the people they are interested in, comment on their messages and forward them to other people. In this way, there is a huge amount of data generated by users on social networks. Besides; in addition to textual sharing, image and video sharing are also available through mobile phones with a camera. In this study, text and image-based semantic inference methods in social networks were investigated. The advantages of different semantic inference methods are emphasized. Finally, information was given on what can be done in this area in the future.

Özet

Sosyal ağların gün geçtikçe daha popüler hale gelmesi sebebiyle, çok sayıda kullanıcı sürekli olarak aktif sosyal ağ kullanıcısı durumundadır. Aktif kullanıcılar sürekli olarak gördüklerini ve düşündüklerini paylaşmaktadır. Sosyal ağ kullanıcıları ilgilendikleri kişileri takip etmekte, mesajlarına yorum yapmakta ve diğer kişilere iletmektedir. Bu şekilde, sosyal ağlarda kullanıcılar tarafından üretilen çok büyük miktarda veri bulunmaktadır. Bunun yanında; kameralı cep telefonları sayesinde metinsel paylaşımlar yanında görüntü ve video paylaşımları da yapılmaktadır. Bu çalışmada, sosyal ağlarda metin ve görüntü tabanlı anlamsal çıkarım yöntemleri incelenmiştir. Farklı anlamsal çıkarım yöntemlerinin, kullanıldığı alanlara göre avantajları vurgulanmıştır. Son olarak da, bu alanda gelecekte ne gibi çalışmalar yapılabileceğine dair bilgiler verilmiştir.

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Cite This Article

BAYRAKDAR, S., YÜCEDAĞ, İ. (2020). INVESTIGATION OF TEXT AND IMAGE BASED SEMANTIC INFERENCE METHODS IN SOCIAL NETWORKS. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, Cilt: 28 Sayı: 3, 283–288. https://ajindex.com/articles/905093

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References (13)

  1. Akaichi, J., (2014). A Medical Social Network for Physicians’ Annotations Posting and Summarization. Social Network Analysis and Mining, 4, 1-8. doi: 10.1007/s13278-014-0225-1.
  2. Al-Abad, A.M., Al-Sahail, B.A., Al-Henaki, B.A., Al-Zaid, D.A., Al-Andas, G.A., Al-Mazroo, R.M. and Al-Ogaiel, R.M., (2009). A Semantic Social Network Service for Educating Saudi Breast Cancer Patients. Ninth IEEE International Conference on Advanced Learning Technologies, July 2009,
  3. Riga, Latvia, p.81-82.
  4. Apaydin, A., Celik, D. and Elci, A., (2010). Semantic Image Retrieval Model for Sharing Experiences in Social Networks. IEEE 34th Annual Computer Software and Applications Conference Workshops, July 2010,
  5. Seoul, South Korea, p.1-6.
  6. Ayadi, M.G., Bouslimi, R. and Akaichi, A., (2016). Medical Image Retrieval Scheme Through A Medical Social Network. Network Modeling Analysis in Health Informatics and Bioinformatics, 5, 1-20. doi: 10.1007/s13721-016-0130-9.
  7. Barnes, J.A., (1969). Graph Theory and Social Networks: A Technical Comment on Connectedness and Connectivity. Sociology, 3(2), 215-232. doi: 10.1177/003803856900300205. Bontcheva, K. and Rout, D., (2014). Making Sense of Social Media Streams Through Semantics: A Survey. Semantic Web, 5(5), 373-403. doi: 10.3233/SW-130110. Bouslimi, R., Ayadi, M.G. and Akaichi, J., (2017). Semantic Medical Image Retrieval in A Medical Social Network. Social Network Analysis and Mining, 7(2), 1-11. doi: 10.1007/s13278-016-0420-3.
  8. Demirci, M.S. ve Sagiroglu, S., (2017). Sosyal Ağ Verilerinin Kullanım Alanları Üzerine Kapsamlı Bir İnceleme. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 5(2), 1-21.
  9. Elahi, N., Karlsen, R. and Younas, W., (2010). Semantic Image Annotation with Social Context. International Conference for Internet Technology and Secured Transactions, November 2010,
  10. London, UK, p.1-7.
  11. Federico, L., Néstor, D. and Oscar, C., (2012). SMITag: A Social Network for Semantic Annotation of Medical Images. XXXVIII Conferencia Latinoamericana En Informatica (CLEI), October 2012,
  12. Medellin, Colombia, p. 1-7.
  13. Kou, F., Du, J., He, Y. and Ye, L., (2016). Social Network Search based on Semantic Analysis and Learning. CAAI Transactions on Intelligence Technology, 1(4), 293-302. doi: 10.1016/j.trit.2016.12.001. Krishnamurthy, M., Mahmood, K. and Marcinek, P., (2016). A Hybrid Statistical and Semantic Model for Identification of Mental Health and Behavioral Disorders using Social Network Analysis. IEEE/ACM International Conference on