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Predicting consumer sentiments from online text

Predicting consumer sentiments from online text. Presenter: Jun-Yi Wu Authors: Xue Bai. 國立雲林科技大學 National Yunlin University of Science and Technology. 2011 DSS. Outline. Motivation Objective Methodology Experiments Conclusion Comments. Motivation.

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Predicting consumer sentiments from online text

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  1. Predicting consumer sentiments from online text Presenter: Jun-Yi Wu Authors: XueBai 國立雲林科技大學 National Yunlin University of Science and Technology 2011 DSS

  2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Comments

  3. 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.

  4. 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. News

  5. Methodology • Bayesian network and Markov blanket • Tabu search • The Markov blanket for a sentiment variable

  6. Methodology • Bayesian network and Markov blanket

  7. Methodology • 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.

  8. Methodology • The Markov blanket for a sentiment variable • The algorithm for learning the Markov Blanket for a sentiment variable is called a Markov Blanket Classifier.

  9. Experiments

  10. Experiments

  11. Experiments

  12. Conclusion • 1 12

  13. Comments • 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 13

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