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 © 2011 Springer-Verlag.