| In recent years, quality safety incidents occurred frequently in our country, it hasaffected not only people’s physical and mental health, but also the stability of society andeven the development of economy. The traditional supervisory systems of quality safetydepend on methods such as sampling inspection, questionnaire or customer complaints.These methods have a lot of disadvantages such as inefficiency and limited coverage, andthey are not enough to solve this growing problem. With the development of Internet, ithas become the main platform for people to express their opinions and comments, includea lot of comments of consumer on product quality safety. The collection and analysis ofthese data is valuable to the inspection and forecasting of quality safety.This thesis designed and implemented a text classifying system which can processthe massive online quality safety feedback information from consumer automatically byusing text classification technology. The system has the following functions:1) Industryclassifying. The system designed and implemented a text multiple classifier based onsupport vector machine algorithm to divide the data by industry and methodize the data, sothat the data is easy to manage.2) Information filtering. The system designed andimplemented a text binary classifier based on Naive Bayes algorithm to process the datafrom the crawler system. Its purpose is to filter the data which is not related to qualitysafety, clean the data and provide the accurate data to the users.3) Risk classifying. Thesystem also designed and implemented a text multiple classifier based on support vectormachine algorithm to specific the problem of quality safety and contribute to theinspection and forecasting of quality safety.The system which is designed and implemented by the thesis has passed the test now.The test and analysis show that the system can filter the unrelated data effectively andclassify data correctly and it has some reference for other classifying system. |
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