Sentiment-Preserving Reduction for Social Media Analysis

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Sentiment-Preserving Reduction for Social Media Analysis
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
Sergio Hernández, Philip Sallis
Added 13 Dec 2011
Updated 13 Dec 2011
Type Journal
Year 2011
Authors Sergio Hernández, Philip Sallis
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