A new polarity clustering algorithm based on semantic criterion function for text of the Chinese commentary

The mining methods for comment text polarity are usually used to adopted supervised learning algorithms, but supervised learning algorithms require significant manual labor marked the training set, and its text set in dealing with will be also faced with dimension disaster, sparse vector, high spatial and temporal complexity, low recall and precision rates that cannot be used for a flood of text polarity classification task. In response to these circumstances, this article will introduce a new polarity clustering algorithm for text of the Chinese commentary, constructed specifically for the Chinese comment on the polarity of the text polarities dictionary meaning of words, a criterion function based on semantic means clustering K-means algorithm. The study is the use of semantic clustering method based on Chinese texts deal with a subjective exploration. The methodologies of experiment, statistics, and analysis are used to do this research. The results of experiment showed that average recall rate of 81.22%, average accuracy rate of 67.76%, indicating that the algorithm is feasible and effective.