Abstract:
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Objective texts like reviews, critics, and comments are mostly written with known sentiments toward each topic in mind. In this work, we propose a novel extension of Latent Dirichlet Allocation model for joint sentiment-topic modeling of text data that extracts latent topic proportions and sentiment proportions toward each of the topics. Incorporating prior knowledge on sentiment words, we propose a new iterative approach based on Gibbs Sampling for approximate estimation of hyperparameters of the introduced three level hierarchical model. We used the new model for (topic) sentiment classification of different synthetic datasets and observed significant improvement over some existing models. Analyzing a real data set also shows the applicability of the proposed model.
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