Keyword Extraction for Search Engine Optimization Using Latent Semantic Analysis

Yıl 2021, , 473 - 479, 01.06.2021

Fahrettin Horasan

https://doi.org/10.2339/politeknik.684377

Öz

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 engine optimization is done to increase the visibility in search engines. In this process, a few keywords are selected from the textual content added to the web page. A responsible person who is knowledgeable about the content and search engine optimization is required to determine these words. Otherwise, an effective optimization study cannot be obtained. In this study, the keyword extraction from textual data with latent semantic analysis technique was performed. The latent semantic analysis technique models the relations between documents/sentences and terms in the text using linear algebra. According to the similarity values of the terms in the resulting vector space, the words that best represent the text are listed. This allows people without knowledge of the SEO process and content to add content that complies with the SEO criteria. Thus, with this method, both financial expenses are reduced and the opportunity to reach the target audience of web pages is provided.

Anahtar Kelimeler

Search engine optimization, keyword extraction, latent semantic analysis, text mining

Kaynakça

• Leavitt, Neal. Network-usage changes push internet traffic to the edge. Computer, 2010, 43.10: 13-15.
• Internet World Stats Internet users of the world: World Internet Usage And Populatıon Statıstıcs, 2019 Mid-Year Estimates . From:www.internetworldstats.com/stats.htm Accessed: Dec 2019.
• Wood, Steve. Web of Deception: Misinformation on the Internet. New Library World, 2003.
• Lili Yana, Zhanji Guia, Wencai Dub,Qingju Guoa, “An Improved PageRank Method based on Genetic Algorithm for Web Search”, 2011
• Meng Cui,Songyun Hu, "Search Engine Optimization Research for Website Promotion" Information Technology, Computer Engineering and Management Sciences (ICM), 2011 International Conference on (Volume:4 )
• John B. Kılloran, "How to Use Search Engine Optimization Techniques to Increase Website Visibility" IEEE Transactıons On Professıonal Communıcatıon, Vol. 56, No. 1, March 2013
• Yalçın, Nursel; Köse, Utku. What is search engine optimization: SEO?. Procedia-Social and Behavioral Sciences, 2010, 9: 487-493.
• Ross A. Malaga, "Worst Practices in Search Engine Optimization" communications of the acm, December 2008, vol. 51 , no. 12
• Google's Search Engine Optimization Starter Guide 2013
• Mittal, Mayank Kumar; Kirar, Neha; Meena, Jasraj. Implementation of Search Engine Optimization: Through White Hat Techniques. In: 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). IEEE, 2018. p. 674-678.
• Aure´lie Gandour, Amanda Regolini, "Web site search engine optimization: a case study of Fragfornet" Library Hi Tech News Number 6 2011, pp. 6-13, q Emerald Group Publishing Limited, 0741-9058, DOI 10.1108/07419051111173874
• Arben Asllani, Alireza Lari, "Using genetic algorithm for dynamic and multiple criteria web-site optimizations" European Journal of Operational Research 176 (2007) 1767–1777
• Justin Boyan, Dayne Freitag, and Thorsten Joachims, "A Machine Learning Architecture for Optimizing Web Search Engines" AAAI Technical Report WS-96-06. Compilation copyright © 1996, AAAI, October 9, 1996
• Kiritchenko, Svetlana; Jiline, Mikhail. Keyword optimization in sponsored search via feature selection. In: New Challenges for Feature Selection in Data Mining and Knowledge Discovery. 2008. p. 122-134.
• Zimniewicz, Michał; Kurowski, Krzysztof; Węglarz, Jan. Scheduling aspects in keyword extraction problem. International Transactions in Operational Research, 2018, 25.2: 507-522.
• Amruta Joshi, Rajeev Motwani "Keyword Generation for Search Engine Advertising", Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006
• Abhishek, Vibhanshu; Hosanagar, Kartik. Keyword generation for search engine advertising using semantic similarity between terms. In: Proceedings of the ninth international conference on Electronic commerce. ACM, 2007. p. 89-94.
• Sordoni, Alessandro, et al. 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. ACM, 2015. p. 553-562.
• Hong, Yuan, et al. Accurate and efficient query clustering via top ranked search results. In: Web Intelligence. IOS Press, 2016. p. 119-138.
• Süzek, Tuğba Önal. Using latent semantic analysis for automated keyword extraction from large document corpora. Turkish Journal of Electrical Engineering & Computer Sciences, 2017, 25.3: 1784-1794.
• Varçin, Fatih; Erbay, Hasan; Horasan, Fahrettin. Latent semantic analysis via truncated ULV decomposition. In: Signal Processing and Communication Application Conference (SIU), 2016 24th. IEEE, 2016. p. 1333-1336.
• Horasan, Fahrettin, et al. Alternate Low-Rank Matrix Approximation in Latent Semantic Analysis. Scientific Programming, 2019, 2019.
• D. I. Martin and M. W. Berry, “Mathematical foundations behind latent semantic analysis,” Handbook of latent semantic analysis, pp. 35–56, 2007.
• M. W. Berry and R. D. Fierro, “Low-rank orthogonal decompositions for information retrieval applications,” Numerical linear algebra with applications, vol. 3, no. 4, pp. 301–327, 1996.
• Duman E., Erbay H., Latent semantic analysis approach for automatic classification of web pages contents, Master Thesis, 2013.
• Shima K, Todoriki M, Suzuki A. SVM-based feature selection of latent semantic features. Pattern Recognition Letters. 2004 Jul 2;25(9):1051-7.
• Uysal AK, Gunal S. Text classification using genetic algorithm oriented latent semantic features. Expert Systems with Applications. 2014 Oct 1;41(13):5938-47.
• Jessup, E. R.; Martin, J. H. Taking a new look at the latent semantic analysis approach to information retrieval. Computational information retrieval, 2001, 2001: 121-144.