Print Email Facebook Twitter Automatic Psychological Text Analysis using Support Vector Machine Classification Title Automatic Psychological Text Analysis using Support Vector Machine Classification Author Park, Jeongwoo (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Intelligent Systems) Contributor Brinkman, W.P. (mentor) Bruijnes, M. (mentor) Hung, H.S. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2021-07-01 Abstract In recent years, there has been an increasing number of patients with mental disorders. A conversational agent is being developed to ensure an easier diagnosis based on the chat between a patient and the agent. The objective of this research is to assess how well Support Vector Machine (SVM) classifies text into its corresponding schema, which are the mental states of the patient. In total, three different classifications have been attempted, Binary, Ordinal, and Per-Questionnaire. The experimental result indicated that SVM is possible to classify 2 out of 7 schema modes, but in general, the performance of SVM was not outperforming with a low f1-score. At the end of the research, SVM was compared to Recurrent Neural Network (RNN) and k-Nearest-Neighbour (kNN) and it turned out that RNN gives the best performance. One of the limitations affecting the result is the quality of the data set. With more correlated labels and a greater size of the data set, improved results can be expected. Subject Text ClassificationSupport Vector MachinePreprocessingWord embedding To reference this document use: http://resolver.tudelft.nl/uuid:cc07a5cf-b575-4a84-b37b-cb17ef7317d6 Part of collection Student theses Document type bachelor thesis Rights © 2021 Jeongwoo Park Files PDF Research_Project_2020_1.pdf 405.72 KB Close viewer /islandora/object/uuid:cc07a5cf-b575-4a84-b37b-cb17ef7317d6/datastream/OBJ/view