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We examine whether aggregate daily Twitter keyword volumes over eight months from November 2011 to June 2012 can be used to predict aggregate daily consumer spending as reported by Gallup. We also examine whether Twitter keyword volume improves predictive ability over prediction based solely on current spending
Search results in technical forums are typically keyword based. The relevance of a link is usually gauged by closest content match. However, it has been shown in literature that users' click behavior is an integral part of deciding the relevance of a search result. Moreover, it is not just the number of clicks that
can meet the venereal-disease suspected patients' privacy need. This paper will use search data in the prediction of the incidence of gonorrhea, begin from theory analysis to reveal the relationship between the Baidu search keyword search volume and gonorrhea incidence, and then apply quantitative empirical analysis
always ignores relativity of the topic. These affect the topic discovery and topic trend. Therefore, combining with the keywords combination and Word2Vec model to strength expression of semantic information in topic clustering, this article sets weighted K-means algorithm for topic discovery. The results show our weighted K
In a sponsored search market, the problem of measuring the intensity of competition among advertisers is increasingly gaining prominence today. Usually, search providers want to monitor the advertiser communities that share common bidding keywords, so that they can intervene when competition slackens. However, to the
number of main keywords (5 inputs) each of which has 4 synonyms based on specific constraints. These inputs have been processed by developing two general models including; Artificial Neural Network Back-propagation optimization technique and Subtractive Clustering technique. Furthermore a third general model have developed
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