Thesis (M.A.)--University of Alberta, 2010. / Title from pdf file main screen (viewed May 16, 2010). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Arts in Humanities Computing, Dept of History and Classics". Includes bibliographical references.

Thesis (M.Arch.)--University of Hong Kong, 2002. / Includes one special report study. Includes bibliographical references. Also available in print.

Sentiment can be defined as a tendency to experience certain emotions in relation to a particular object or person. Sentiment may be expressed in writing, in which case determining that sentiment algorithmically is known as sentiment analysis. Sentiment analysis is often applied to Internet texts such as product reviews, websites, blogs, or tweets, where automatically determining published feeling towards a product, or service is very useful to marketers or opinion analysts. The main goal of sentiment analysis is to identify the polarity of natural language text. This thesis sets out to examine quantitatively the factors that have an effect on sentiment analysis. The factors that are commonly used in sentiment analysis are text features, sentiment lexica or resources, and the machine learning algorithms employed. The main aim of this thesis is to investigate systematically the interaction between sentiment analysis factors and machine learning algorithms in order to improve sentiment analysis performance as compared to the opinions of human assessors. A software system known as TJP was designed and developed to support this investigation. The research reported here has three main parts. Firstly, the role of data pre-processing was investigated with TJP using a combination of features together with publically available datasets. This considers the relationship and relative importance of superficial text features such as emoticons, n-grams, negations, hashtags, repeated letters, special characters, slang, and stopwords. The resulting statistical analysis suggests that a combination of all of these features achieves better accuracy with the dataset, and had a considerable effect on system performance. Secondly, the effect of human marked up training data was considered, since this is required by supervised machine learning algorithms. The results gained from TJP suggest that training data greatly augments sentiment analysis performance. However, the combination of training data and sentiment lexica seems to provide optimal performance. Nevertheless, one particular sentiment lexicon, AFINN, contributed better than others in the absence of training data, and therefore would be appropriate for unsupervised approaches to sentiment analysis. Finally, the performance of two sophisticated ensemble machine learning algorithms was investigated. Both the Arbiter Tree and Combiner Tree were chosen since neither of them has previously been used with sentiment analysis. The objective here was to demonstrate their applicability and effectiveness compared to that of the leading single machine learning algorithms, Naïve Bayes, and Support Vector Machines. The results showed that whilst either can be applied to sentiment analysis, the Arbiter Tree ensemble algorithm achieved better accuracy performance than either the Combiner Tree or any single machine learning algorithm.

Procura-se identificar, a partir do processo de documentação museográfica de uma coleção têxtil, mecanismos metodológicos da Ciência da Informação que auxiliem o trabalho de documentação, organização e disponibilização da informação em museus. Analisa-se a história das áreas, suas especificidades, seus princípios e metodologias de forma a identificar a possibilidade de suas interfaces prováveis. Dentre os vários aspectos da documentação museográfica, destacam-se as contribuições da Linguística Documentária em sua interface com a Terminologia, no que diz respeito à circunscrição dos termos que servem à denominação das peças analisadas. / The aim is try to identify methodological mechanisms of The Science of Information to assist the documentation, organization and provision of information job in museums, through the museographic process of documentation of collection textiles. It was analyzed the history of the areas, its specificities, principles and methodology to identify the possibility of its possible interfaces. Within some aspects of museographic documentation highlight the contributions of Linguistics Documentary in its interface with the Terminology with respect to the circumscription of terms that serve to name the parts analyzed.

Procura-se identificar, a partir do processo de documentação museográfica de uma coleção têxtil, mecanismos metodológicos da Ciência da Informação que auxiliem o trabalho de documentação, organização e disponibilização da informação em museus. Analisa-se a história das áreas, suas especificidades, seus princípios e metodologias de forma a identificar a possibilidade de suas interfaces prováveis. Dentre os vários aspectos da documentação museográfica, destacam-se as contribuições da Linguística Documentária em sua interface com a Terminologia, no que diz respeito à circunscrição dos termos que servem à denominação das peças analisadas. / The aim is try to identify methodological mechanisms of The Science of Information to assist the documentation, organization and provision of information job in museums, through the museographic process of documentation of collection textiles. It was analyzed the history of the areas, its specificities, principles and methodology to identify the possibility of its possible interfaces. Within some aspects of museographic documentation highlight the contributions of Linguistics Documentary in its interface with the Terminology with respect to the circumscription of terms that serve to name the parts analyzed.

Thesis (M.Sc.)--York University, 2003. Graduate Programme in Earth and Space Science & Engineering. / Typescript. Includes bibliographical references (leaves 119-128). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://proquest.umi.com/pqdweb?index=0&did=913494611&SrchMode=1&sid=1&Fmt=13&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1174573026&clientId=5220

Thesis (M.S.)--Rochester Institute of Technology, 1990. / "Annotated bibliography": leaves 85-91.

Thesis (Ph. D.)--University of Missouri-Columbia, 1997. / Typescript. Vita. Includes bibliographical references (leaves [256]-267). Also available on the Internet.

Thesis (M.S.)--Rochester Institute of Technology, 1987. / Typescript. Includes bibliographical references (leaves viii-xi).

Thesis (Ph. D.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 88-93). Also available on the World Wide Web.