Institute for Software Research International
School of Computer Science, Carnegie Mellon University
Sentiment Extraction from Unstructured Text using
Xue Bai, Rema Padman, Edoardo Airoldi
In this paper, we propose a two-stage Bayesian algorithm that is able to capture the dependencies among words, and, at the same time, finds a vocabulary that is efficient for the purpose of extracting sentiments. Experimental results on the Movie Reviews data set show that our algorithm is able to select a parsimonious feature set with substantially fewer predictor variables than in the full data set and leads to better predictions about sentiment orientations than several state-of-the-art machine learning methods.
Our findings suggest that sentiments are captured by conditional dependence relations among words, rather than by keywords or high-frequency words.