The Design and Development of Public Bus Complaint Classification Process for Service Problem Tagging
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Abstract
Bangkok Mass Transit Authority (BMTA) has a webboard for service user complaints on public buses. This information is important and improving service efficiency. Now, the complaints are increasing, more different and lexical error problem affect the accuracy of the complaints classification. Because the person has to analyze the data by self. This research aims 1) to the design and development of public bus complaint classification process from BMTA webboard by Thai dictionary-based approach and keywords selection by TF-IDF to create a corpus-based divided into 4 classes: driving class, person class, vehicle class and schedule class. The result of text classification algorithms, found that it was at a very good level. 2) to evaluation the accuracy of the public bus complaint classification. Then, keywords matching with user complaints again and increase the accuracy of keywords by Levenshtein Distance. In case that found the incorrectly keywords for create complaint classification tag. The accuracy assessment of complaint classification is a very good level, especially one complaint issue. The results show performance of dictionary-based approach on Thai word segmentation is suitable for the terminology that has definite scope. However, found problems in same keywords to be duplicated in some class. For example, loudness can be attribute from the person class or the vehicle-equipment class according to the origin of the sound. As well as the unique compounding of Thai language affects to the accuracy of word tokenization. These results will present an approach to data classification and will benefit to those responsible to improve the service for customers.
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