Automatic Psychological Text Analysis using Support Vector Machine Classification

Automatic Psychological Text Analysis using Support Vector Machine Classification

Park, Jeongwoo (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Intelligent Systems)

Brinkman, W.P. (mentor)
Bruijnes, M. (mentor)
Hung, H.S. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2021-07-01

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.

Text Classification
Support Vector Machine
Preprocessing
Word embedding

http://resolver.tudelft.nl/uuid:cc07a5cf-b575-4a84-b37b-cb17ef7317d6

Student theses

bachelor thesis

© 2021 Jeongwoo Park