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Text sentiment classification can be extensively applied to information retrieval, text filtering, online tracking evaluation, the diagnoses of public opinions and chat systems. In this paper, a kinds of hybrid methods, based on category distinguishing ability of words and information gain, is adopted to feature selection. For examining the impact of(More)
With the rapid growth of e-commerce, product reviews on the Web have become an important information source for customers’ decision making when they intend to buy some product. As the reviews are often too many for customers to go through, how to automatically classify them into different sentiment orientation categories (i.e. positive/negative) has become(More)
This paper introduces granular computing (GrC) into formal concept analysis (FCA). It provides a unified model for concept lattice building and rule extraction on a fuzzy granularity base for different granulations. One of the strengths of GrC is that larger granulations help to hide some specific details, whereas FCA in a GrC context can prevent losses due(More)
The vast subjective texts spreading all over the Internet promoted the demand for text sentiment classification technology. A well-known fact that often weakens the performance of classifiers is the distribution imbalance of review texts on the positive–negative classes. In this paper, we pay attention to the sentiment classification problem of imbalanced(More)
This paper introduces concept lattice and granular computing into ontology learning, and presents a unified research model for ontology building, ontology merging and ontology connection based on the domain ontology base in different granulations. In this model, as the knowledge in the lowest and most basic level, the domain ontology base is presented(More)
Clustering on categorical data streams is a relatively new field that has not received as much attention as static data and numerical data streams. One of the main difficulties in categorical data analysis is lacking in an appropriate way to define the similarity or dissimilarity measure on data. In this paper, we propose three dissimilarity measures: a(More)