Decision Making from Streaming Data

Abstract

In a crowdsourcing environment, judgment analysis involves gathering opinions from a diverse online crowd to reach a consensus. Traditional methods work onlywhenall opinions are available fromthe start. Our goal is to develop amethod for judgment analysis that works as opinions stream in. This dissertation is divided into two parts, each focusing on judgment analysis in a crowdsourcing environment. In the first chapter, we treat all questions and annotators as having equal weight. In the second chapter, we consider different weights for both questions and annotators to make final decisions.We present the first algorithm capable of analyzing crowdsourced opinions in real-time. Tested on two datasets, our method achieves accuracy close to majority voting while requiring only a small amount of space. In the second algorithm We tested it on two datasets, showing it matches the accuracy of majority voting and uses minimal space. This work advances judgment analysis in crowdsourcing, providing a more reliable solution than first for real-time decision-making with online crowdsourced opinions