Managing information technology (IT) for one-to-one customer interaction
Information and Management
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Clustering Algorithms
Interactive methods for taxonomy editing and validation
Proceedings of the eleventh international conference on Information and knowledge management
Frequent term-based text clustering
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Design and implementation of the UIMA common analysis system
IBM Systems Journal
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
COBRA - Mining Web for Corporate Brand and Reputation Analysis
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Multi-taxonomy: Determining Perceived Brand Characteristics from Web Data
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
COA: finding novel patents through text analysis
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A study on text clustering algorithms based on frequent term sets
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Conceptualizing smart city with dimensions of technology, people, and institutions
Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times
Hi-index | 0.00 |
As a result of the growth of the Internet, the amount of available information is exponentially increasing. However, increasing the amount of information does not imply increasing usefulness. Furthermore, as the complexity of business relationships increases, there is a natural tendency toward less structured interaction between entities. This highlights the growing relevance of unstructured information in documenting the interactions of organizations and individuals. Analyzing and making sense of this unstructured information space requires more than text-mining algorithms; it requires a strategic approach. We propose a unified approach that addresses a variety of information space analytics problems. Our method for making sense of unstructured data is described by six steps that are analogous to the algebraic order of operations PEMDAS (parenthesis, exponent, multiplication, division, addition, and subtraction). These basic text-mining operations can be combined in many interesting ways to handle a diverse set of problems, and just as in algebra, it is critical that these operations be performed in the correct order to guarantee a meaningful result. In this paper, we describe how PEMDAS has been implemented within organizations to enable decisions that produced measurable business value.