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Predicting consumer sentiments from online text

Presenter: Jun-Yi Wu　Authors: Xue Bai

2011 DSS

國立雲林科技大學National Yunlin University of Science and Technology

Intelligent Database Systems Lab

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I. M.Outline

Motivation Objective Methodology Experiments Conclusion Comments

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Intelligent Database Systems Lab

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I. M.Motivation In recent years, due to the sheer volume of online reviews and

news corpora available in digital form. An accurate method not only predicting consumer

sentiments but also can be used to reduce the risk.

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Intelligent Database Systems Lab

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I. M.Objective

To propose a heuristic search-enhanced Markov blanket model that is able to capture the dependencies among words and provide a vocabulary that is adequate for the purpose of extracting sentiments.

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News

Intelligent Database Systems Lab

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I. M.Methodology

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Bayesian network and Markov blanket Tabu search The Markov blanket for a sentiment variable

Intelligent Database Systems Lab

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I. M.Methodology

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Bayesian network and Markov blanket

Intelligent Database Systems Lab

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I. M.Methodology

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Tabu search Tabu search is a meta-heuristic search method that is able to guide

traditional local search methods to escape local optima with the assistance of adaptive memory.

Tabu search starts with a feasible solution and chooses the best move according to an evaluation function, while taking steps to ensure that the method does not revisit a solution previously generated.

Intelligent Database Systems Lab

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I. M.Methodology

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The Markov blanket for a sentiment variable The algorithm for learning the Markov Blanket for a sentiment variable

is called a Markov Blanket Classifier.

Intelligent Database Systems Lab

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I. M.Experiments

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I. M.Experiments

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I. M.Experiments

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Intelligent Database Systems Lab

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I. M.Conclusion

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Intelligent Database Systems Lab

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I. M.Comments

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Advantage This method yields predictive performance comparable and in many

cases superior to those of other state-of-the-art classification methods.

Application Sentiment analysis