Creating Usable Customer Intelligence from Social Media Data: Network Analytics meets Text Mining Killian Thiel Tobias Kötter Dr. Michael Berthold Dr. Rosaria Silipo Phil Winters Killian.Thiel@uni-konstanz.de Tobias.koetter@uni-konstanz.de Michael.Berthold@uni-konstanz.de Rosaria.Silipo@KNIME.com Phil.Winters@KNIME.com Copyright © 2012 by KNIME.com AG all rights reserved Revision: 120403F page 1 Table of Contents Creating Usable Customer Intelligence from Social Media Data: Network
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Starbucks customers are examined in our research. The demographics that are being cross-examined for contribution are age, days per month, cups per day, and income. The statistical tool we use to better understand the predicting power these factors play in the contribution of money deposited into the prepaid cards is a multiple variable regression. The regression, which is ran using statistical analysis on excel takes a random set of data with uncertain characteristics and finds correlations in the data
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Financial Analysis and Planning, serves the function of integrating much of the material we have covered. That topic is Lease Financing. There is a lot of material on the structure of the lease and on the accounting treatment of leases, but the analytical focus will be on the lease-buy decision. The lease-buy decision is actually a financing decision. The analysis of the advisability of a lease typically follows a prior decision to acquire an asset (based on an investment decision analysis). In lease
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Similarity based Analysis of Networks of Ultra Low Resolution Sensors Relevance: Pervasive computing, temporal analysis to discover behaviour Method: MDS, Co-occurrence, HMMs, Agglomerative Clustering, Similarity Analysis Organization: MERL Published: July 2006, Pattern Recognition 39(10) Special Issue on Similarity Based Pattern Recognition Summary: Unsupervised discovery of structure from activations of very low resolution ambient sensors. Methods for discovering location geometry from movement
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Writing Tips For Economics Research Papers∗ Plamen Nikolov, Harvard University † June 10, 2013 1 General Tips about Writing Style When I read your term papers, I look for your ability to motivate your question using economic logic, your ability to critically analyze the past literature, and your ability to recognize empirical problems as they arise. In particular, it is important that your term paper demonstrates that you are more knowledgeable, analytic, and sophisticated about the
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Chapter 14 Factor analysis 14.1 INTRODUCTION Factor analysis is a method for investigating whether a number of variables of interest Y1 , Y2 , : : :, Yl, are linearly related to a smaller number of unobservable factors F1, F2, : : :, Fk . The fact that the factors are not observable disquali¯es regression and other methods previously examined. We shall see, however, that under certain conditions the hypothesized factor model has certain implications, and these implications in turn can be
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1. We now know what the concepts involving cluster analysis are, the different types of clusterings and clusters, the basic algorithms etc. That leads us to the second paper, titled: "Cluster analysis in marketing research: review and suggestions for application". Where the book chapter mainly explains the theory underlying cluster analysis, this paper actually focuses on the practical issues regarding the use and validation of cluster analytic methods. This part of the presentation is built up
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Sr. No | Title | Author | Country & year | Variables | Techniques | Major findings | Limitations | Future direction | 1 | Shocks are causes of turnover: What they are and what organization can do to control them
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modifying loyalty and meeting customer needs and decrease costs by amending inventory procedures. The initiative should be implemented within specific time period along with the involvement of following stakeholder representatives, as with their contribution the program will be
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individual differences towards students academic aspect. Astin’s Input-Environment-Output (I-E-O) model is adapted to explain relationships between academic achievement and students input and learning environments. The model allows analysis of of each component’s contribution on academic achievement which is based on students’ cummulative grade point average (CGPA) on a four point scale. Involvement theory which posits that students development is related to the quantity and quality of their involvement
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