Answer Comments As Reviews: Predicting Acceptance By Measuring Valence On Stack Exchange

Online communication has increased the need to interpret complex emotions rapidly; due to the volatility of the data involved, machine learning tasks that process text aim to address the related challenges. Exploring text in comments that supports ideas through computational methods is a logical next step considering similar research for these question-and-answer sites. A lack of current algorithms that can accurately predict accepted answers with equal votes suggests a gap in this knowledge. Measuring collaborative signals in comments finds common keywords to move a problem toward a solution.Using a dataset from questions posted to Stack Exchange on the subject area of machine learning, the researchers constructed a model using the comments of posts made on accepted answers. Intending to discover whether predictions of marked solutions are accurate by treating the comments as reviews, the researchers find a reduction in error by incorporating reviews of answers as a feature in the predictive algorithm.