Gizli Anlamsal Analiz ile Arama Motorları için Anahtar Kelime Çıkarma

Devasa bilgi yığınının bulunduğu internet dünyasında artık istenilen bilgiye erişmek zor hale geldi. Arama motorları bu zorluğun altından kalkmak için çaba sarf etmektedirler. Ancak arama motorlarında hedef kitlesine ulaşamayan bir web sayfası popüler hale gelememektedir. Arama motorlarındaki görünürlüğün artırılması için arama motoru optimizasyonu yapılır. Bu süreçte web sayfasına eklenen metinsel içeriklerden anahtar kelimeler seçilir. Bu kelimelerin belirlenmesi için hem içerik hakkında ve hem de arama motoru optimizasyonu konusunda bilgili bir sorumlu kişi gereklidir. Böyle olmadığı durumlarda etkili bir optimizasyon çalışması elde edilemez. Bu çalışmada gizli anlamsal analiz tekniği ile metinsel verilerden anahtar kelime çıkarma işlemi gerçekleştirilmiştir. Gizli anlamsal analiz yöntemi, metin içerisindeki doküman/cümle ve terimler arasındaki ilişkileri lineer cebir yönüyle modellemektedir. Elde edilen vektör uzayındaki terimlerin benzerlik değerlerine göre metini en iyi temsil edilen kelimeler listelenir. Bu işlem SEO süreci ve içerik hakkında bilgisi olmayan insanların da SEO kriterlerine uygun içerik eklemelerine imkan tanıyacaktır. Dolayısıyla, bu yöntemle hem maddi gider azaltılmış hem de web sayfalarının hedef kitlesine ulaşma fırsatı sağlanmıştır.

Keyword extraction for search engine optimization using latent semantic analysis

It is now difficult to access desired information in the Internet world. Search engines are always trying to overcome this difficulty.However, web pages that cannot reach their target audience in search engines cannot become popular. For this reason, search engineoptimization is done to increase the visibility in search engines. In this process, a few keywords are selected from the textual contentadded to the web page. A responsible person who is knowledgeable about the content and search engine optimization is requiredto determine these words. Otherwise, an effective optimization study cannot be obtained. In this study, the keyword extraction fromtextual data with latent semantic analysis technique was performed. The latent semantic analysis technique models the relationsbetween documents/sentences and terms in the text using linear algebra. According to the similarity values of the terms in theresulting vector space, the words that best represent the text are listed. This allows people without knowledge of the SEO processand content to add content that complies with the SEO criteria. Thus, with this method, both financial expenses are reduced andthe opportunity to reach the target audience of web pages is provided.

PDF

___

[1] Leavitt, Neal ,“Network-usage changes push internet traffic to the edge.”, Computer, 43.10: 13-15 (2010).
[2] Internet World Stats Internet users of the world: World Internet Usage And Populatıon Statıstıcs, “2019 MidYear Estimates”, www.internetworldstats.com ( 2019).
[3] Wood, Steve, “Web of Deception: Misinformation on the Internet”. New Library World, (2003).
[4] Yan, L., Gui, Z., Du, W., & Guo, Q., “An improved PageRank method based on genetic algorithm for web search.”, Procedia Engineering, 15: 2983-2987, (2011).
[5] Cui, M., & Hu, S., “Search engine optimization research for website promotion”. In 2011 International Conference of Information Technology, Computer Engineering and Management Sciences, 4: 100-103, (2011).
[6] Killoran, J. B., “How to use search engine optimization techniques to increase website visibility.”, IEEE Transactions on professional communication, 56(1): 50-66, (2013).
[7] Yalçın, N., & Köse, U., “What is search engine optimization: SEO?.”, Procedia-Social and Behavioral Sciences, 9: 487-493, (2010).
[8] Malaga, R. A. , “Worst practices in search engine optimization. Communications of the ACM, 51(12): 147-150, (2008).
[9] “Google's Search Engine Optimization Starter Guide”, (2013).
[10] Mittal, M. K., Kirar, N., & Meena, J. “Implementation of Search Engine Optimization: Through White Hat Techniques.”, In 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 674-678), (2018).
[11] Gandour, A., & Regolini, A., “Web site search engine optimization: a case study of Fragfornet.”, Library Hi Tech News, (2011).
[12] Asllani, A., & Lari, A., “Using genetic algorithm for dynamic and multiple criteria web-site optimizations.”, European journal of operational research, 176(3): 1767-1777, (2007).
[13] Boyan, J., Freitag, D., & Joachims, T., “A machine learning architecture for optimizing web search engines.”, In AAAI Workshop on Internet Based Information Systems, (1996).
[14] Kiritchenko, S., & Jiline, M., Keyword optimization in sponsored search via feature selection. In New Challenges for Feature Selection in Data Mining and Knowledge Discovery, 122-134, (2008).
[15] Zimniewicz, M., Kurowski, K., & Węglarz, J., "Scheduling aspects in keyword extraction problem.”, International Transactions in Operational Research, 25(2): 507-522, (2018).
[16] Joshi, A., & Motwani, R., “Keyword generation for search engine advertising.”, In Sixth IEEE International Conference on Data Mining-Workshops (ICDMW'06, 490-496, (2006).
[17] Abhishek, V., & Hosanagar, K., “Keyword generation for search engine advertising using semantic similarity between terms.”, In Proceedings of the ninth international conference on Electronic commerce, 89- 94, (2007).
[18] Sordoni, A., Bengio, Y., Vahabi, H., Lioma, C., Grue Simonsen, J., & Nie, J. Y., “A hierarchical recurrent encoder-decoder for generative context-aware query suggestion.” In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 553-562, (2015).
[19] Hong, Y., Vaidya, J., Lu, H., & Liu, W. M. Accurate and efficient query clustering via top ranked search results. In Web Intelligence, 14(2): 119-138, IOS Press, (2016).
[20] Süzek, T. Ö. Using latent semantic analysis for automated keyword extraction from large document corpora. Turkish Journal of Electrical Engineering & Computer Sciences, 25(3): 1784-1794, (2017).
[21] Varçın, F., Erbay, H., & Horasan, F., “Latent semantic analysis via truncated ULV decomposition.”, In 2016 24th Signal Processing and Communication Application Conference (SIU), 1333-1336,. IEEE, (2016).
[22] Horasan, F., Erbay, H., Varçın, F., & Deniz, E. “Alternate Low-Rank Matrix Approximation in Latent Semantic Analysis.”, Scientific Programming, (2019).
[23] Martin, D. I., & Berry, M. W., “Mathematical foundations behind latent semantic analysis.”, Handbook of latent semantic analysis, 35-55, (2007).
[24] Berry, M. W., & Fierro, R. D., “Low‐rank Orthogonal Decompositions for Information Retrieval Applications.”, Numerical linear algebra with applications, 3(4): 301-327, (1996).
[25] Duman E., Erbay H., “Latent semantic analysis approach for automatic classification of web pages contents.”, Master Thesis, (2013).
[26] Shima, K., Todoriki, M., & Suzuki, A., “SVM-based feature selection of latent semantic features.”, Pattern Recognition Letters, 25(9): 1051-1057, (2004).
[27] Uysal, A. K., & Gunal, S. Text classification using genetic algorithm oriented latent semantic features. Expert Systems with Applications, 41(13): 5938-5947, (2014).
[28] Jessup, E. R., & Martin, J. H.,“Taking a new look at the latent semantic analysis approach to information retrieval.”, Computational information retrieval, 121- 144, (2001)