A qualitative and quantitative method for predicting sentiment toward deployed U.S. forces

Abstract

The ability to automatically predict likelihood of reaction to specific events and situational awareness is important to many military and commercial applications. Gauging population sentiment for targeted response areas and having the ability to predict or control sentiment within these areas is invaluable. Review of reception towards deployed forces must be analyzed, especially in areas vital for U.S. national interests. Predicting population behavior is critical for success and must include a qualitative as well as a quantitative solution. Additionally, a feedback mechanism is needed for periodically updating reception towards presence of U.S. Forces over time. We propose a method for predicting sentiment towards deployed U.S. Forces in near real time, to efficiently propitiate manpower resources, allocate equipment assets, and reduce cost of analyses. Sentiment prediction is becoming an increasingly important and feasible task based on social media, open source data, physical imagery and abundance of video data feeds. Predicting reaction to events can be time consuming. Locating the most likely affected areas is very tedious, requiring much human labor effort, and it is often difficult to obtain the best information on a timely basis. An efficient tool would be helpful to rapidly parse text that has been extracted from an intelligent algorithm in order to evaluate the population sentiment for the targeted area. Multiple data inputs and artificial intelligence (AI) algorithms are required in order to support sound decision making theory. The goal of our system, called GlobalSite, is to deliver trustworthy threat analysis systems and services that understand situations, while being a vital tool for continuing mission operations information.