Analysis And Application On Sentiment Of Short Texts

Human language in addition to passing the objective facts,but also carries the natural expression of human emotion information,text sentiment classification is through text mining and analysis positions,beliefs,opinions,emotions and other subjective information,to express their views to the microblogging platforms such as the representative of Chinese short text messages was the explosive growth in the face of these short texts rapid growth,how to effectively organize bearer a large amount of data,how to obtain the latest information on specific requirements to become a serious problem.Text sentiment classification is through text mining and analysis of positions,beliefs,opinions,emotions and other subjective information,emotional tendencies of the text to make category judgments.We could understand that people can make for a certain thing,organization or individual evaluation and emotional tendencies.Based on the analysis and summary of sentiment analysis on short texts,this thesis puts forward a method toa coarse-grained sentiment analysis method based on sentiment wordsextraction and LDA feature representation was proposed.Firstly,sentimentwords were extracted by sentiment lexicon.Then,LDA making process modeling,in order to solve the short text categorization problem LDA is not satisfactory,we set the number of topics within the range of 2?T,find the optimal solution on different topics classification results.Using SVM method on the basis of the results,sorted,experimental corpus comparison test,the classification performance than a single article LDA method has some improvement.Extensive experiments conducted on Chinese short messages and news titles show that the new method proposed for Chinese short-text classification can obtain a higher classification performance comparing with the conventional classification methods.On the basis of the above study,combined with the web technology,a student opinion sentiment analysis system is designed and implemented.The test result shows that the system is accuracy for short texts.