Sentiment analysis on medical treatment of depression
1University of Oulu, Faculty of Information Technology and Electrical Engineering, Department of Information Processing Science, Information Processing Science
|Online Access:||PDF Full Text (PDF, 1.6 MB)|
|Persistent link:|| http://urn.fi/URN:NBN:fi:oulu-201611103002
|Publish Date:|| 2016-11-16
|Thesis type:||Master's thesis
Applying ICT approach to contribute to improving human’s life is a good purpose for researchers (Pannel, 1993). Thus, implementing products or services with new techniques can be quite an interesting and meaningful topic. Nature language processing is a mature technique may apply machine learning and this technique has already been applied to many applications to server people such as Siri, Chatbot and Google Now. Sentiment analysis is a subject in nature language processing, however, it has not been applied to many fields in our daily life.
Depression is a mental disease which caused a lot of trouble in sociality (Hamilton,1960). It causes a huge damage in people’s daily life in the mental aspect instead of the physical painful. And it is also proved by (Help, 2013) that this kind of mental causing a lot of troubles to patients. Quite many researchers are working to find some better treatments for it. However, similar to any other mental disease, there are many treatments existing for different patients and finding the best treatment for a patient can be a quite difficult job. Thus, in this research, I’m trying to validate the function of the sentiment analysis system by applying the data about depression.
In order to achieve this problem, I have defined three research questions which lead to solving this problem. (1) What is the best algorithm for implementing the sentiment analysis system? (2) What is the best existing sentiment lexicon library which can be applied to implement the sentiment analyzing system? (3) How to implement the sentiment analysis system with a selected sentiment lexicon library? During the research process, I review the literature which is related to these questions to find out the answers.
After all, I selected an open source the sentiment lexical library named SentiWordNot3.0 which was implemented based on an algorithm has the feature of Kth-Nearest Neighbor algorithm and Support Vector Machine. And it proved that following approach can be actually used in sentiment analysis in the medical domain.
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