Abstract
Online marketing is one of the best practices used to establish a brand and to increase its popularity. Advertisements are used in a better way to showcase the company’s product/service and give rise to a worthy online marketing strategy. Posting an advertisement on utilitarian web pages helps to maximize brand reach and get a better feedback. Now-a-days companies are cautious of their brand image on the Internet due to the growing number of Internet users. Since there are billions of Web sites on the Internet, it becomes difficult for companies to really decide where to advertise on the Internet for brand popularity. What if, the company advertise on a page which is visited by less number of the interested (for a particular type of product) users instead of a web page which is visited by more number of the interested users?—this doubt and uncertainty—is a core issue faced by many companies. This research paper presents a Brand analysis framework and suggests some experimental practices to ensure efficiency of the proposed framework. This framework is divided into three components—(1) Web site network formation framework—a framework that forms a Web site network of a specific search query obtained from resultant web pages of three search engines-Google, Yahoo & Bing and their associated web pages; (2) content scraping framework—it crawls the content of web pages existing in the framework-formed Web site network; (3) rank assignment of networked web pages—text edge processing algorithm has been used to find out terms of interest and their occurrence associated with search query. We have further applied sentiment analysis to validate positive or negative impact of the sentences, having the search term and its associated terms (with reference to the search query) to identify impact of web page. Later, on the basis of both—text edge analysis and sentiment analysis results, we assigned a rank to networked web pages and online social network pages. In this research work, we present experiments for ‘Motorola smart phone,’ ‘LG smart phone’ and ‘Samsung smart phone’ as search query and sampled the Web site network of top 20 search results of all three search engines and examined up to 60 search results for each search engine. This work is useful to target the right online location for specific brand marketing. Once the brand knows the web pages/social media pages containing high brand affinity and ensures that the content of high affinity web page/social media page has a positive impact, we advertise at that respective online location. Thus, targeted brand analysis framework for online marketing not only has benefits for the advertisement agencies but also for the customers.
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References
Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R (2011) Sentiment analysis of twitter data. In: Proceedings of the workshop on languages in social media, Association for Computational Linguistics, pp 30–38
Arora A, Taneja V, Parashar S, Mishra A (2016) Cross-domain based event recommendation using tensor factorization. Open Computer Science 6(1):126–137
Bansal S, Gupta C, Arora A (2016) User tweets based genre prediction and movie recommendation using LSI and SVD. In: Contemporary computing (IC3), 2016 Ninth international conference on IEEE, pp 1–6
Behl D, Handa S, Arora A (2014) A bug mining tool to identify and analyze security bugs using Naive Bayes and tf-idf. In: Optimization, reliability, and information technology (ICROIT), 2014 international conference on IEEE, pp 294–299
Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. CS224N Project report, Stanford, vol 1, p 12
Goeldi A (2014) Website network and advertisement analysis using analytic measurement of online social media content. U.S. Patent No. 7,974,983, 5 July 2011
Jamali S, Rangwala H (2009) Digging digg: Comment mining, popularity prediction, and social network analysis. In: Web information systems and mining, 2009. WISM 2009. International conference on IEEE, pp 32–38
Merriman DA, O’connor KJ (1999) Method of delivery, targeting, and measuring advertising over networks. U.S. Patent No. 5,948,061, 7 Sept 1999
Nigam K, Lafferty J, McCallum A (1999) Using maximum entropy for text classification. In: IJCAI-99 workshop on machine learning for information filtering, vol. 1, pp 61–67
Pang B, Lee L, Vaithyanathan S (2002) Thumbs up?: Sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing, Association for Computational Linguistics, vol 10, pp 79–86
Siersdorfer S, Chelaru S, Pedro JS, Altingovde IS, Nejdl W (2014) Analyzing and mining comments and comment ratings on the social web. ACM Trans Web (TWEB) 8(3):17
Spencer J, Uchyigit G (2012) Sentimentor: Sentiment analysis of twitter data. In: Proceedings of European conference on machine learning and principles and practice of knowledge discovery in databases, pp 56–66
Tan S, Cheng X, Wang Y, Xu H (2009) Adapting naive bayes to domain adaptation for sentiment analysis. In: European Conference on Information Retrieval. Springer, Berlin Heidelberg, pp 337-349
Yessenov K, Misailovic S (2009) Sentiment analysis of movie review comments. Methodology 17:1–7
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Aggrawal, N., Ahluwalia, A., Khurana, P. et al. Brand analysis framework for online marketing: ranking web pages and analyzing popularity of brands on social media. Soc. Netw. Anal. Min. 7, 21 (2017). https://doi.org/10.1007/s13278-017-0442-5
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DOI: https://doi.org/10.1007/s13278-017-0442-5