The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper we present the work done on social media analysis to predict civil unrest using keyword filtering. The information given on the social media is delivered to every person within the fraction of seconds. This rapid circulation of information and the people opinions through social platform affects or create
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
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
The study aimed at analyzing the keywords of the Macau Special Administrative Region's 2012 and 2013 annual policy addresses. The contribution of the study included the following two points. First, the study used the text mining method in order to explore the content of policy address. Second., the study applied the
framework, selected keywords, composed the keywords into composite index, found a strong correlation, and finally a result of prediction sales was given in the end of the paper.
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
Spatial event forecasting from social media is potentially extremely useful but suffers from critical challenges, such as the dynamic patterns of features (keywords) and geographic heterogeneity (e.g., spatial correlations, imbalanced samples, and different populations in different locations). Most existing approaches
from twitter can be used to predict the sales of iPhones. Based on a conceptual model of social data consisting of social graph (actors, actions, activities, and artefacts) and social text (topics, keywords, pronouns, and sentiments), we develop and evaluate a linear regression model that transforms iPhone tweets into a
level and is gathered through open-source methods. These methods include collecting articles related to different countries and incidents and then parsing them to find frequencies of keywords. This frequency counts provide the input data for three different predictive approaches for generating factor specific, culture
, e.g. genres, product categories, keywords) must be used. We describe a method that maps such entity (e.g. user or item) attributes to the latent features of a matrix (or higher-dimensional) factorization model. With such mappings, the factors of a MF model trained by standard techniques can be applied to the new-user and
the industry. Much of what is available tells that information such as cookies and browsing history are used to target customers, associating keywords with specific groups of inventory. We designed and conducted a web-based shopping experiment with fifty participants to observe how people of different backgrounds and
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.