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Three Essays On Opinion Mining Of Social Media Texts, Shuyuan Deng
Three Essays On Opinion Mining Of Social Media Texts, Shuyuan Deng
Theses and Dissertations
This dissertation research is a collection of three essays on opinion mining of social media texts. I explore different theoretical and methodological perspectives in this inquiry. The first essay focuses on improving lexicon-based sentiment classification. I propose a method to automatically generate a sentiment lexicon that incorporates knowledge from both the language domain and the content domain. This method learns word associations from a large unannotated corpus. These associations are used to identify new sentiment words. Using a Twitter data set containing 743,069 tweets related to the stock market, I show that the sentiment lexicons generated using the proposed method …
Adverse Drug Event Detection, Causality Inference, Patient Communication And Translational Research, Balaji Polepalli Ramesh
Adverse Drug Event Detection, Causality Inference, Patient Communication And Translational Research, Balaji Polepalli Ramesh
Theses and Dissertations
Adverse drug events (ADEs) are injuries resulting from a medical intervention related to a drug. ADEs are responsible for nearly 20% of all the adverse events that occur in hospitalized patients. ADEs have been shown to increase the cost of health care and the length of stays in hospital. Therefore, detecting and preventing ADEs for pharmacovigilance is an important task that can improve the quality of health care and reduce the cost in a hospital setting. In this dissertation, we focus on the development of ADEtector, a system that identifies ADEs and medication information from electronic medical records and the …
Disease Name Extraction From Clinical Text Using Conditional Random Fields, Omid Ghiasvand
Disease Name Extraction From Clinical Text Using Conditional Random Fields, Omid Ghiasvand
Theses and Dissertations
The aim of the research done in this thesis was to extract disease and disorder names from clinical texts. We utilized Conditional Random Fields (CRF) as the main method to label diseases and disorders in clinical sentences. We used some other tools such as MetaMap and Stanford Core NLP tool to extract some crucial features. MetaMap tool was used to identify names of diseases/disorders that are already in UMLS Metathesaurus. Some other important features such as lemmatized versions of words, and POS tags were extracted using the Stanford Core NLP tool. Some more features were extracted directly from UMLS Metathesaurus, …