Research Of Text Sentiment Classification

A text is automatically classified as positive or negative sentiment through text sentiment classification, i.e. mining and analyzing subjective information in the text, such as standpoint, view, mood, and so on. As more and more people express their viewpoints on web, text sentiment classification becomes more and more important.This paper presents and implements a text sentiment classification algorithm, which contains two steps, subjectivity classification and polarity classification.Subjectivity classification contains two procedures:training and classification. In the training procedure, feature presentations for sentences are obtained from labeled training text sets via text preprocessing, text presentation and feature selection; then, text subjectivity classification model is obtained via subjectivity classification model training algorithm. In the classification procedure, feature presentations for sentences to be classified are obtained via text preprocessing, text present and feature selection; then, text subjectivity classification algorithm together with classification model is used to classify the sentences as an objective text subset and a subjective text subset; at last, the results are corrected by dynamic programming.In the training procedure of polarity classification, a source domain labeled text set and a target domain unlabeled text set are combined as a training set, feature presentations for sentences of the training set are obtained via text preprocessing, text presentation and SCL feature selection based on pivot features; then a text polarity classification model is obtained via polarity classification model training algorithm. In the classification procedure, feature presentations for sentences to be classified are obtained from subjective text via text preprocess text present and SCL feature selection based on pivot features; then text polarity classification algorithm together with classification model is used to classify the sentences as a positive sentence subset and a negative sentence subset.Experiments indicate that the precision of the preliminary subjectivity classification is 94.7%; the precision of the Bayes classifier based on the dynamic programming correction is 95.8%; the LAMP (Logistic Average Misclassification Percentage) of the SCL algorithm based on pivot feature is 0.16, which is lower than normal SCL algorithm.