No abstract

For the attribute-weighted based naive Bayesian classification algorithms, the selection of the weight directly affects the classification results. Based on this, the drawbacks of the TFIDF feature selection approaches in sentiment classification for the microblogs are analyzed, and an improved algorithm named TF-D(t)-CHI is proposed, which applies statistical calculation to obtain the correlation degree between the feature words and the classes. It presents the distribution of the feature items by variance in classes, which solves the problem that the short-texts contain few feature words while the high frequency feature words have too high weight. Experimental result indicate that TF-D(T)-CHI based naive Bayesian classification for feature selection and weight calculation has better classification results in sentiment classification for microblogs.

The application of GIS (geographic information system) and webservice in travel information field is an inevitable trend. WebGIS is a type of GIS, integration of these two technologies. This paper provides an overview and information useful for approaching WebGIS. And, the system was described for travel information based on WebGIS. According to the requirements of visitors, a model is created to meet their needs, so the information which displayed on the web can be update. Then, the use of the method was discussed, and some technique were used to analyze data which collect form users. Java Script Object Notation (JSON) was used to transfer the data between server and client. Finally, Custom Personalized Travel System was displayed.