The Key Technology And System Implementation On Grain Public Opinion Analysis

Along with the development of Internet，network has become an important platform ofgrain information communication where more and more government agencies and groupsexpress their emotions and ideas of grain information, and has become the grain’s free marketplace of ideas, so it is very necessary to effectively analysis or supervise the topics andexpressions on the internet. Grasping the grain public opinion trends through themagnanimous network text data is of much significance to actively guiding public opinion insociety. The key technology and system implementation on grain public opinion analysis hasbeen an important research topic in the field of national grain security.The thesis in-depth analysis the technologies of grain public opinion, such as graininformation acquisition, grain information preprocessing, grain public opinion analyzing, andso on. Studying the key technologies involved in grain public opinion analyzing system,including the text feature selection and the feature weight algorithms. The research workinvolves several aspects as follows:（1） Mutual information algorithm of text feature selection is strongly influenced bythe critical value，so mutual information tends to choose rare feature. To overcome theshortage, this paper proposes an improved MI approach based on Term Frequency, CohesionInformation Among classes, Coupling Information Inside a class for feature distribution. Anexperiment is carried out and the results show that the improved TF-CA-CI method can bettercontrol randomness of feature selection in the lower dimension, and effectively improve theclassification performance of the low-dimensional feature selection.（2） The pros and cons of term weight algorithm has great impact on theclassification results; The defect of traditional TFIDF algorithm is that it doesn’t consider theterm frequency and characteristic distribution. This paper introduces a new improvement ideaof Word frequency weaken and Weight Adjustment Factor of feature distribution information,then puts forward a new term weight algorithm TFTD after in-depth analysis of improvementmethods, and contrast the classification performance of improved TFTD algorithm to originalTFIDF algorithm and4kinds of representational improved algorithm from the50d to1000dby experiment. The results of experiment show that TFTD algorithm has better classificationeffect than the other algorithms from low dimension to high dimension. （3） It is based on the above research results, the thesis designs and implements grainpublic opinion system of network.