Automatic structuring and retrieval of large text files
Communications of the ACM
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
Semantic clustering: Identifying topics in source code
Information and Software Technology
Text classification from unlabeled documents with bootstrapping and feature projection techniques
Information Processing and Management: an International Journal
Customer-adapted coupon targeting using feature selection
Expert Systems with Applications: An International Journal
An empirical examination of the science-technology relationship in the biotechnology industry
Journal of Engineering and Technology Management
A semantic term weighting scheme for text categorization
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Topics modeling based on selective Zipf distribution
Expert Systems with Applications: An International Journal
Global data mining: An empirical study of current trends, future forecasts and technology diffusions
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
User-oriented ontology-based clustering of stored memories
Expert Systems with Applications: An International Journal
Using latent topics to enhance search and recommendation in Enterprise Social Software
Expert Systems with Applications: An International Journal
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
An SAO-based text mining approach to building a technology tree for technology planning
Expert Systems with Applications: An International Journal
Predicting e-commerce company success by mining the text of its publicly-accessible website
Expert Systems with Applications: An International Journal
Improved multilevel security with latent semantic indexing
Expert Systems with Applications: An International Journal
Technology classification with latent semantic indexing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Protecting research and technology from espionage
Expert Systems with Applications: An International Journal
Web mining based extraction of problem solution ideas
Expert Systems with Applications: An International Journal
Weak signal identification with semantic web mining
Expert Systems with Applications: An International Journal
Quantitative cross impact analysis with latent semantic indexing
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The aim of cross impact analysis (CIA) is to predict the impact of a first event on a second. For organization's strategic planning, it is helpful to identify the impacts among organization's internal events and to compare these impacts to the corresponding impacts of external events from organization's competitors. For this, literature has introduced compared cross impact analysis (CCIA) that depicts advantages and disadvantages of the relationships between organization's events to the relationships between competitors' events. However, CCIA is restricted to the use of patent data as representative for competitors' events and it applies a knowledge structure based text mining approach that does not allow considering semantic aspects from highly unstructured textual information. In contrast to related work, we propose an internet based environmental scanning procedure to identify textual patterns represent competitors' events. To enable processing of this highly unstructured textual information, the proposed methodology uses latent semantic indexing (LSI) to calculate the compared cross impacts (CCI) for an organization. A latent semantic subspace is built that consists of semantic textual patterns. These patterns are selected that represent organization's events. A web mining approach is used for crawling textual information from the internet based on keywords extracted from each selected pattern. This textual information is projected into the same latent semantic subspace. Based on the relationships between the semantic textual patterns in the subspace, CCI is calculated for different events of an organization. A case study shows that the proposed approach successfully calculates the CCI for technologies processed by a governmental organization. This enables decision makers to direct their investments more targeted.