Showing papers by "Yongfeng Huang published in 2013"

Review rating prediction based on the content and weighting strong social relation of reviewers

Abstract: Review rating is more helpful than review binary classification for many decision processes such as consumption decision-making, company product quality tracking and public opinion mining. In the review rating, reviewers are influenced not only by their own subjective feelings, but also by others' rating to the same product. Existing review rating prediction methods are mainly based on the content of reviews, which only consider the subjective factors of reviewers, but not consider the impact of other people in the social relations of reviewers. Based on it, we propose a review rating prediction method by incorporating the character of reviewer's social relations, as regularization constraints, into content-based methods. In addition, we further propose a method to classify the social relations of reviewers into strong social relation and ordinary social relation. For strong social relation of reviewers, we give higher weight than ordinary social relation when incorporating the two social relations into content-based methods. Experiments on two real movie review datasets demonstrate that the method of considering different social relations has better performance than the content-based methods and the method of considering social relations as a whole.

Lead-lag analysis via sparse co-projection in correlated text streams

Abstract: Correlated topical trend detection is very useful in analyzing public and social media influence. In this paper, we propose an algorithm that can both detect the correlation and discover the corresponding keywords that trigger the correlation. To detect the correlation, we use a projection vector to project two text streams onto the same space, and then use a least square cost function to regress one text stream over the other with different time lags. To extract the corresponding keywords, we impose the non-negative sparsity constraints over the projection parameters. In addition, we present an accelerated algorithm based on Nesterov's method to efficiently solve the optimization problem. In our experiments, we use both syntehtic and real data sets to demonstrate the advantages and capabilities of the proposed algorithm over CCA on the follower link prediction problem.

Chinese reviews sentiment classification based on quantified sentiment lexicon and fuzzy set

Abstract: As the most extensively studied topic in sentiment analysis, sentiment classification has mainly two types of methods: supervised learning and unsupervised learning. As one of unsupervised learning methods, sentiment lexicon-based method plays a very important role in sentiment classification. However, there are two problems in existing sentiment lexicon-based method. Firstly, Sentiment words are only divided into positive and negative categories in existing sentiment lexicons, but polarity intensity of sentiment words is not quantified. Secondly, sentiment classification is formulated as an either-or problem, yet the fuzziness of sentiment categories is not considered. In order to solve the two problems, we propose a new method based on quantified sentiment lexicon and fuzzy set. We firstly construct some quantified sentiment lexicons based on three Chinese sentiment lexicons, and then calculate sentiment intensity of Chinese reviews by quantified sentiment lexicon, finally, we classify Chinese reviews based on fuzzy classifier. Experiment results in two review datasets demonstrate that our method outperforms the state-of-the-art methods.