Classifying Short Text Complaints with nBD-SVM Model
Bengong Yu1,2,Yangnan Chen1(),Ying Yang1,2
1(School of Management, Hefei University of Technology, Hefei 230009, China) 2(Key Laboratory of Process Optimization & Intelligent Decision-making, Ministry of Education, Hefei University of Technology, Hefei 230009, China)
[Objective] This paper tries to find an effective way to classify the non-structured and short-text business complaints, aiming to improve the efficiency of corporate problem solving. [Methods] We first combined the topic model and distributed representation technique to construct a SVM input space vector. Then, we integrated ensemble learning method to build the nBD-SVM text classification model. [Results] We examined the proposed model with business complaint texts and found its precision reached 81.83%, which is much higher than the traditional methods. [Limitations] We only evaluate our model with complaints from one company. [Conclusions] The proposed nBD-SVM model could process short text business complaints effectively.
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Bengong Yu,Yangnan Chen,Ying Yang. Classifying Short Text Complaints with nBD-SVM Model. Data Analysis and Knowledge Discovery, DOI：10.11925/infotech.2096-3467.2018.0758.
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