A novel social media competitive analytics framework with sentiment benchmarks

被引:143
|
作者
He, Wu [1 ]
Wu, Harris [1 ]
Yan, Gongjun [2 ]
Akula, Vasudeva [3 ]
Shen, Jiancheng [4 ]
机构
[1] Old Dominion Univ, Coll Business & Publ Adm, Dept Informat Technol & Decis Sci, Norfolk, VA 23529 USA
[2] Univ So Indiana, Dept Management & Informat Sci, Romain Coll Business, Evansville, IN 47712 USA
[3] VOZIQ, Business Consulting, Reston, VA 20190 USA
[4] Old Dominion Univ, Coll Business & Publ Adm, Dept Finance, Norfolk, VA 23529 USA
关键词
Social media analytics; Competitive analytics; Sentiment benchmarks; Text mining; Sentiment analysis; User-generated data; Social media; Marketing intelligence; Big data; Social media monitoring; BUSINESS INTELLIGENCE; REVIEWS; SYSTEMS;
D O I
10.1016/j.im.2015.04.006
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In today's competitive business environment, there is a strong need for businesses to collect, monitor, and analyze user-generated data on their own and on their competitors' social media sites, such as Facebook, Twitter, and blogs. To achieve a competitive advantage, it is often necessary to listen to and understand what customers are saying about competitors' products and services. Current social media analytics frameworks do not provide benchmarks that allow businesses to compare customer sentiment on social media to easily understand where businesses are doing well and where they need to improve. In this paper, we present a social media competitive analytics framework with sentiment benchmarks that can be used to glean industry-specific marketing intelligence. Based on the idea of the proposed framework, new social media competitive analytics with sentiment benchmarks can be developed to enhance marketing intelligence and to identify specific actionable areas in which businesses are leading and lagging to further improve their customers' experience using customer opinions gleaned from social media. Guided by the proposed framework, an innovative business-driven social media competitive analytics tool named VOZIQ is developed. We use VOZIQ to analyze tweets associated with five large retail sector companies and to generate meaningful business insight reports. (C) 2015 Elsevier B.V. All rights reserved.
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页码:801 / 812
页数:12
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