UCSC-SOE-09-35: Measuring Contributions to Email-Based Discussion Groups

Ian Pye, Luca de Alfaro
11/23/2009 09:00 AM
Computer Science
Email-based discussion groups are a vast source of non-canonical crowd sourced information. However, due to their open nature (anyone can post), evaluating the quality of answers is challenging. In this work, we develop a framework for analyzing author contributions to email-based discussion groups.

Sentiment analysis is the process of extracting the overall feeling from a body of text. We present a novel technique which applies sentiment analysis to evaluate the quality of answers. We present six novel algorithms and compare their results to a manually calculated baseline, two machine learning algorithms, and two algorithms based on link analysis. We find that by using sentiment analysis, our algorithms out perform both the machine learning and link analysis approaches in most experiments. We also find that a simple text-based approach without sentiment analysis is surprisingly powerful.