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Sentiment analysis of tweets is an application of mining Twitter, and is growing in popularity as a means of determining public opinion. Machine learning algorithms are used to perform sentiment analysis; however, data quality issues such as high dimensionality, class imbalance or noise may negatively impact classifier performance. Machine learning techniques exist for targeting these problems, but have not been applied to this domain, or have not been studied in detail. In this thesis we discuss research that has been conducted on tweet sentiment classification, its accompanying data concerns, and methods of addressing these concerns. We test the impact of feature selection, data sampling and ensemble techniques in an effort to improve classifier performance. We also evaluate the combination of feature selection and ensemble techniques and examine the effects of high dimensionality when combining multiple types of features. Additionally, we provide strategies and insights for potential avenues of future work. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2015 / FAU Electronic Theses and Dissertations Collection

Thesis (Ph.D.)--University of Missouri-Columbia, 2004. / Typescript. Vita. Includes bibliographical references (leaves [184]-190). Also available on the Internet.

Thesis (Ph.D.)--University of Missouri-Columbia, 2004. / Typescript. Vita. Includes bibliographical references (leaves [184]-190). Also available on the Internet.

No / Quality assessment (QA) requires high levels of domain-specific experience and knowledge. QA tasks for toxicological data are usually performed by human experts manually, although a number of quality evaluation schemes have been proposed in the literature. For instance, the most widely utilised Klimisch scheme1 defines four data quality categories in order to tag data instances with respect to their qualities; ToxRTool2 is an extension of the Klimisch approach aiming to increase the transparency and harmonisation of the approach. Note that the processes of QA in many other areas have been automatised by employing expert systems. Briefly, an expert system is a computer program that uses a knowledge base built upon human expertise, and an inference engine that mimics the reasoning processes of human experts to infer new statements from incoming data. In particular, expert systems have been extended to deal with the uncertainty of information by representing uncertain information (such as linguistic terms) as fuzzy sets under the framework of fuzzy set theory and performing inferences upon fuzzy sets according to fuzzy arithmetic. This paper presents an experimental fuzzy expert system for toxicological data QA which is developed on the basis of the Klimisch approach and the ToxRTool in an effort to illustrate the power of expert systems to toxicologists, and to examine if fuzzy expert systems are a viable solution for QA of toxicological data. Such direction still faces great difficulties due to the well-known common challenge of toxicological data QA that "five toxicologists may have six opinions". In the meantime, this challenge may offer an opportunity for expert systems because the construction and refinement of the knowledge base could be a converging process of different opinions which is of significant importance for regulatory policy making under the regulation of REACH, though a consensus may never be reached. Also, in order to facilitate the implementation of Weight of Evidence approaches and in silico modelling proposed by REACH, there is a higher appeal of numerical quality values than nominal (categorical) ones, where the proposed fuzzy expert system could help. Most importantly, the deriving processes of quality values generated in this way are fully transparent, and thus comprehensible, for final users, which is another vital point for policy making specified in REACH. Case studies have been conducted and this report not only shows the promise of the approach, but also demonstrates the difficulties of the approach and thus indicates areas for future development. / U 7th Framework Programme Integrated Project “Integrated In Silico Models for Prediction of Human Repeated Dose Toxicity of Cosmetics to Optimise Safety” (COSMOS). Grant Number: 266835. Cosmetics Europe.

Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Systems Science and Industrial Engineering, 2008. / Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 77-82).

Made available in DSpace on 2014-12-17T15:47:52Z (GMT). No. of bitstreams: 1 CassioHF_DISSERT.pdf: 1503543 bytes, checksum: f8c5d434affbf0ef65dde0a97d3eb071 (MD5) Previous issue date: 2010-02-22 / Despite the emergence of other forms of artificial lift, sucker rod pumping systems remains hegemonic because of its flexibility of operation and lower investment cost compared to other lifting techniques developed. A successful rod pumping sizing necessarily passes through the supply of estimated flow and the controlled wear of pumping equipment used in the mounted configuration. However, the mediation of these elements is particularly challenging, especially for most designers dealing with this work, which still lack the experience needed to get good projects pumping in time. Even with the existence of various computer applications on the market in order to facilitate this task, they must face a grueling process of trial and error until you get the most appropriate combination of equipment for installation in the well. This thesis proposes the creation of an expert system in the design of sucker rod pumping systems. Its mission is to guide a petroleum engineer in the task of selecting a range of equipment appropriate to the context provided by the characteristics of the oil that will be raised to the surface. Features such as the level of gas separation, presence of corrosive elements, possibility of production of sand and waxing are taken into account in selecting the pumping unit, sucker-rod strings and subsurface pump and their operation mode. It is able to approximate the inferente process in the way of human reasoning, which leads to results closer to those obtained by a specialist. For this, their production rules were based on the theory of fuzzy sets, able to model vague concepts typically present in human reasoning. The calculations of operating parameters of the pumping system are made by the API RP 11L method. Based on information input, the system is able to return to the user a set of pumping configurations that meet a given design flow, but without subjecting the selected equipment to an effort beyond that which can bear / Apesar do surgimento de outras t?cnicas de eleva??o artificial de petr?leo, sistemas de bombeio mec?nico mant?m-se hegem?nicos devido ? sua flexibilidade de atua??o e custo menor de investimento se comparada com outras t?cnicas de eleva??o desenvolvidas. Um dimensionamento de bombeio mec?nico bem sucedido necessariamente passa pelo atendimento da vaz?o prevista e pelo desgaste controlado dos equipamentos de bombeio utilizados na configura??o montada. Entretanto, a concilia??o destes elementos mostrase particularmente desafiadora, sobretudo para a maioria dos projetistas que lidam com este trabalho, que n?o possuem ainda a experi?ncia necess?ria para chegar a bons projetos de bombeio em tempo h?bil. Mesmo com a exist?ncia de diversos aplicativos computacionais no mercado com o objetivo de facilitar esta tarefa, eles precisam enfrentar um exaustivo processo de tentativa e erro at? chegar a combina??o mais adequada de equipamentos para instala??o no po?o. A proposta do presente trabalho consiste em desenvolver um sistema especialista no dimensionamento de sistemas de bombeio mec?nico. Ele tem a miss?o de guiar um engenheiro de petr?leo na tarefa de selecionar um conjunto de equipamentos apropriados ao contexto provido pelas caracter?sticas do ?leo que ser? produzido. Caracter?sticas como o n?vel de separa??o do g?s, presen?a de elementos corrosivos, possibilidade de produ??o de areia e de parafina??o s?o levados em considera??o na escolha da bomba de fundo, coluna de hastes e unidade de bombeio, bem como as caracter?sticas de opera??o dos mesmos. Ele ? capaz de aproximar seu processo de infer?ncia da forma do racioc?nio humano, o que gera resultados mais pr?ximos daqueles obtidos por um especialista. Para tanto, suas regras de produ??o foram elaboradas com base na teoria dos conjuntos fuzzy, capazes de modelar conceitos imprecisos tipicamente presentes no racioc?nio humano. Os c?lculos dos par?metros operacionais do sistema de bombeio s?o feitos por meio do m?todo API RP 11L. Com base em informa??es de entrada, o sistema ? capaz de retornar ao usu?rio um conjunto de configura??es de bombeio mec?nico que atendam uma determinada vaz?o de projeto, por?m sem submeter os equipamentos selecionados a um esfor?o al?m daquele que possam suportar