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Twitter sentiment analysis provides organizations with real-time monitoring of public feelings towards particular products and events related to them. Most existing research is focused on extraction of sentiment features through analysis of lexical and syntactic features that are expressed explicitly through words, emoticons, exclamation marks etc. Single machine learning classifiers are usually employed...
Traditional support vector machine treats all samples using the same weight. Therefore it is very sensitive to noisy data. While the fuzzy support vector machine assigns lower weights to the samples which make small contributions to classification, thus it is beneficial to reduce the effects of noisy and unimportant data on the classification accuracy rate. In this paper, we propose a novel fuzzy...
Focusing on Chinese subject-predicate constructions, this paper analyzes the limitations of Selectional-Preference based metaphor recognition and proposes a new metaphor recognition model which is based on Semantic Relation Patterns. The model constructs Semantic Relation Pattern by integrating six types of semantic relations between a subject head and other subject heads in a subject-predicate cluster...
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