Analysis of Competitor Intelligence in the Era of Big Data: An Integrated System Using Text Summarization Based on Global Optimization

Abstract

Automatic text summarization can be applied to extract summaries from competitor intelligence (CI) corpora that organizations create by gathering textual data from the Internet. Such a representation of CI text is easier for managers to interpret and use for making decisions. This research investigates design of an integrated system for CI analysis which comprises clustering and automatic text summarization and evaluates quality of extractive summaries generated automatically by various text-summarization techniques based on global optimization. This research is conducted using experimentation and empirical analysis of results. A survey of practicing managers is also carried out to understand the effectiveness of automatically generated summaries from CI perspective. Firstly, it shows that global optimization-based techniques generate good quality extractive summaries for CI analysis from topical clusters created by the clustering step of the integrated system. Secondly, it shows the usefulness of the generated summaries by having them evaluated by practicing managers from CI perspective. Finally, the implication of this research from the point of view of theory and practice is discussed.

Optimization of Tourism Information Analysis System Based on Big Data Algorithm

“…Tourists can choose scenic spots and routes according to their preferences, and their tour preferences are more personalized. However, tourist routes planned by travel agencies are mostly for the purpose of making profits, and the design of relevant tourist routes is mainly based on factors such as time, traffic, and scenic spots, and other factors in specific profit space in specific areas [3,4]. At present, tourism route planning mostly focuses on regional tourism planning [5,6], which is aimed at a large tourist scenic spot.…”