Using text classification and multiple concepts to answer e-mails

Abstract

In text mining, the applications domain of text classification techniques is very broad to include text filtering, word identification, and web page classification, etc. Through text classification techniques, documents can be placed into previously defined classifications in order to save on time costs especially when manual document search methods are employed. This research uses text classification techniques applied to e-mail reply template suggestions in order to lower the burden of customer service personnel in responding to e-mails. Suggested templates allows customer service personnel, using a pre-determined number of templates, to find the needed reply template, and not waste time in searching for relevant answers from too much information available. Current text classification techniques are still single-concept based. This research hopes to use a multiple concept method to integrate the relationship between concepts and classifications which will thus allow easy text classification. Through integration of different concepts and classifications, a dynamically unified e-mail concept can recommend different appropriate reply templates. In so doing, the differences between e-mails can be definitely determined, effectively improving the accuracy of the suggested template. In addition, for e-mails with two or more questions, this research tries to come up with an appropriate reply template. Based on experimental verification, the method proposed in this research effectively proposes a template for e-mails of multiple questions. Therefore, using multiple concepts to display the document topic is definitely a clearer way of extracting information that a document wants to convey when the vector of similar documents is used.