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Sentiment Extraction From Unstructured Text Using Tabu Search-Enhanced Markov Blanket

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Executive Summary

This white paper proposes 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. It also reveals that experimental results on the Movie Reviews data set show that the 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 and suggests that sentiments are captured by conditional dependence relations among words, rather than by keywords or high-frequency words.

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