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Sentiment-Preserving Reduction for Social Media Analysis

  • Sergio Hernández
  • Philip Sallis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)

Abstract

In this paper, we address the problem of opinion analysis using a probabilistic approach to the underlying structure of different types of opinions or sentiments around a certain object. In our approach, an opinion is partitioned according to whether there is a direct relevance to a latent topic or sentiment. Opinions are then expressed as a mixture of sentiment-related parameters and the noise is regarded as data stream errors or spam. We propose an entropy-based approach using a value-weighted matrix for word relevance matching which is also used to compute document scores. By using a bootstrap technique with sampling proportions given by the word scores, we show that a lower dimensionality matrix can be achieved. The resulting noise-reduced data is regarded as a sentiment-preserving reduction layer, where terms of direct relevance to the initial parameter values are stored

Keywords

Opinion Mining Topic Modeling Latent Dirichlet Allocation Sentiment Analysis Latent Dirichlet Allocation Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sergio Hernández
    • 1
  • Philip Sallis
    • 2
  1. 1.Laboratorio de Procesamiento de Información GeoespacialUniversidad Católica del MauleTalcaChile
  2. 2.Geoinformatics Research CentreAuckland University of TechnologyAucklandNew Zealand

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