Comparative Study of Sentiment Detection Techniques for Business Analytics

Abstract

As the amount of data proliferates, businesses are faced with a plethora of decision support opportunities and often times lack a prescribed set of techniques to speed up or even handle analysis opportunities. The primary purpose of this research is to identify the most effective sentiment detection technique using an experimentation approach, involving comparison studies. The second part of the research is to make a useful and original contribution by developing a conceptual framework containing relevant business questions with automated problem-solving and visualization approaches for business decision support. Implementation of this software program includes development of a conceptual framework, containing relevant business questions, and realizing its practical implementation for business decision support. Based on our experience working in business analytics in the insurance industry, we selected five questions to focus on: 1) what if any relationship exists between daily social sentiment and daily stock price, 2) what if any relationship exists between positive social sentiment volumes and sales volumes, 3) what if any relationship exists between negative social sentiment volumes and sales volumes, 4) what if any relationships exist between quarterly financial results and sentiment, and 5) what if any relationship exists between the overall state of the financial market and stock price.

The development of a business decision support framework was accomplished by investigating two possible approaches to designing and validating components of the proposed framework: a system design approach or an experimentation approach. A system design approach involves making an initial, informed choice of data analysis and visualization techniques for each question, designing and prototyping a decision support system that covers all questions, studying the effectiveness of the system, determining any necessary modifications, and based on the results, redesigning the system. An experimentation approach, on the other hand, required making and testing hypothesis about appropriate data analysis and visualization techniques for one business question at a time, developing the solutions, testing the solutions with business analysts, and revising as necessary. Subsequent research followed the latter of these approaches toward the goal of developing a conceptual framework and realizing its practical implementation for business decision support.