<p>Online reviews are frequently utilized to determine a product's quality before purchase along with the photographs and one-to-five star ratings. The research addressed the two distinct problems observed in the review systems. </p> <p>First, due to thousands of reviews for a product, the different characteristics of customer evaluations, such as consumer sentiments, cannot be understood by manually reading only a few reviews. Second, from these reviews, it is extremely hard to understand the change in these sentiments and other important product aspects over the years (temporal analysis). To address these problems, the study focused on 2 main research parts.</p> <p>Part one of the research was focused on answering how topic modelling and sentiment analysis can work together to give deeper understanding on attribute-based product review. The second part compared different topic modelling approaches to evaluate the performances and advantages of emerging NLP models. For this purpose, a dataset consisting of 469 publicly accessible Amazon evaluations of the Kindle E-reader and 15,000 reviews of iPhone products was utilized to examine sentiment Analysis and Topic modelling. Latent Dirichlet Allocation topic model and BERTopic topic model were used to perform topic modelling and to acquire the diverse topics of concern. Sentiment Analysis was carried out to better understand each topic's positive and negative tones. Topic analysis of Kindle user evaluations revealed the following major themes: (a) leisure consumption, (b) utility as a gift, (c) pricing, (d) parental control, (e) reliability and durability, and (f) charging. While the main themes emerged from the analysis of iPhone reviews depended on the model and year of the device, some themes were found to be consistent across all the iPhone models including (a) Apple vs Android (b) utility as gift and (c) service. The study's approach helped to analyze customer reviews for any product, and the study results provided a deeper understanding of the product's strengths and weaknesses based on a comprehensive analysis of user feedback useful for product makers, retailers, e-commerce platforms, and consumers.</p>

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The lack of success of the gun control movement and the success of the gun rights movement in the United States have prompted research into the root causes. Although the political infrastructure, organizational resources, and public interest prove to be important factors in a social movement's success, how each social movement frames their arguments is extremely important for proposing policy initiatives and garnering support. In order to understand how gun control and gun rights organizations frame their arguments this study does two things: (1) performs topic modeling on the six gun control organizations' and three gun rights organizations' press statements to see the frames that each social movement engages in, and (2) identifying these frames in the most popular gun control and gun rights organizations on Facebook to predict likes, comments, and shares. This study is able to identify the top frames in the gun control and gun rights social movements and see how followers of each of these movements engage with each of these frames on Facebook.

The particle size distribution of a powder plays a crucial role in the performance of bat- teries with powder-based electrodes and requires reliable and practical analysis. The aim of this project was therefore to develop a protocol for analysis of the particle size dis- tribution and composition of powders used in the electrodes of Nilar’s batteries. The analytical methods described in the protocol permits practical applications, such as com- paring the particle size distribution and composition of pristine and reclaimed powders with the manufacturer’s data as quality control.  A literature survey was initially conducted to select appropriate analysis methods for this project. After performing several practical trials, X-ray diffraction, X-ray fluorescence, laser diffraction analysis, and scanning electron microscopy were the techniques included in the protocol. X-ray diffraction showed potential in obtaining the crystallite size of the powders, but other techniques are required to confirm the results. X-ray fluorescence analysis was found to produce fairly similar values as those given by the manufacturer. Scanning electron microscopy was used to analyse the particle size distribution with the help of an image processing software. Complementary data of the smaller particle sizes was obtained using laser diffraction analysis.

電子工業為台灣最具競爭力之產業,使得電子類股在集中市場成交比重高達 69.49%,可見電子類股的波動足以對整個台股市場造成相當大的影響。而許多研究指出,網路上的文本訊息藉由社會網路的催化而快速傳遞,會對群眾情緒造成影響,進而影響股價波動,故對於投資者而言,如果能快速分析大量網路財經文本來推測投資大眾情緒進而預測股價走勢,即可提升獲利。然而,每天有近百篇的財經文本產生,傳統的人工抽樣分析方式效率不彰且過於耗力, 已不足以負荷此巨量資料。 過去文本情感分析的研究中已證實監督式學習方法可以透過簡單量化的方式達到良好的分類效果,但監督式學習方法所使用的訓練資料集須有事先定義好的已知類別,故其有無法預期未知類別的限制,造成無法判斷文本中可能存在的未知主題,所以本研究提出一套針對財經文本的混合監督式學習與非監督式學習之情感分析方法,透過非監督式學習將 2014 整年度的電子工業財經文本進行文本主題判別、情緒指數計算與情緒傾向標注。之後配合視覺化工具作趨勢線圖分析,找出具有領先指標特性之主題,接著再用監督式學習將其結合國際指標、總體經濟指標、台股指標、技術指標等,建立分類模型以預測台灣電子類股價指數走勢。 在實驗結果中,主題標注方面,本研究發現因文本數量遠大於議題詞數量造成 TFIDF 矩陣過於稀疏,使得 TFIDF-Kmeans 主題模型分類效果不佳;而文本具有多主題之特性造成 NPMI-Concor 分群之議題詞過於複雜不易歸納,然而LDA 主題模型基於所有主題被所有文章共享的特性,使得在字詞分群與主題分類準確度都優於 TFIDF-Kmeans 和 NPMI-Concor 主題模型,分類準確度高達 98%,故後續採用 LDA 主題模型進行主題標注。情緒傾向標注方面,證實本研 究擴充後的情感詞集比起 NTUSD 有更好的字詞極性判斷效果,計算出的情緒 指數之趨勢線也較投資人常用的 MACD 之趨勢線更符合電子類股價指數之趨 勢。此外,亦發現並非所有文本的情緒指數皆具有領先特性,僅企業營運主題與總體經濟主題之文本的情緒指數能提前反應電子類股價指數趨勢,故本研究用此二主題之文本的情緒指數來建立分類模型。 接著,本研究透過比較情緒指數結合技術指標之分類模型與單純技術指標分類模型的準確率發現,前者較後者高出 7%的準確率。進一步結合間接情緒指標的分類模型更有高達 71%準確率,故證實了情感分析確實能有效提升電子股價類股指數趨勢預測準確度,以提升投資人之投資報酬率。 / The electronic industry is the most competitive industry in Taiwan, and its large volume could have strong influence on the whole stock market. Many research show that text documents on the Internet have great effect on public emotion, and the public emotion could also affect the stock price. For investors, it is important to know how to analyze the potential emotion in text documents then use this information to predict the stock trend. However, the traditional way to analyze text documents by human resource cannot afford the large volume of financial text documents on the Internet. In past Sentimental Analysis research, supervised method is proven as a method could reach high accuracy, but there are limits about predicting the future trend. This research found a solution which mixed supervised and unsupervised methods to deal with these large financial text documents. First, we use unsupervised method to find out the topic of documents, and then calculate the sentimental index to judge the document’s emotional direction. After that we will produce trend line charts by visualization tools to find out which theme documents’ sentiment index are leading indicators. Furthermore, we use supervised method to integrate the sentimental index with other 24 indirect sentimental index to build the prediction model. According to the result, we found that LDA model’s performance is better than TFIDF-Kmeans model and NPMI-Concor mode because of document characteristic. Besides, sentimental dictionary I build has higher accuracy than NTUSD on judging word polarity. The trend of sentimental index and Taiwan electronic sub-index(TE) to each other is more similar than MACD line and TE to each other. We also discover that the sentiment index produced from documents about enterprise operation and macroeconomics are leading indicators, so we use these to build prediction model. Moreover, we found that the prediction model which include the sentiment index better than which only include the technical indicators. As mentioned above, the sentimental index could make the prediction of Taiwan electronic sub-index trend be more accurate and promote the return of investment.

A qualidade microbiológica de medicamentos é fundamental para garantir sua eficácia e segurança. Os métodos convencionais para identificação microbiana em produtos não estéreis são amplamente utilizados, entretanto são demorados e trabalhosos. O objetivo deste trabalho é desenvolver método microbiológico rápido (MMR) para a identificação de contaminantes em produtos farmacêuticos utilizando a espectrofotometria de infravermelho com transformada de Fourier com reflectância total atenuada (FTIR-ATR). Análise de componentes principais (PCA) e análise de discriminantes (LDA) foram utilizadas para obter um modelo de predição com a capacidade de diferenciar o crescimento de oriundo de contaminação por Bacillus subtilis (ATCC 6633), Candida albicans (ATCC 10231), Enterococcus faecium (ATCC 8459), Escherichia coli (ATCC 8739), Micrococcus luteus (ATCC 10240), Pseudomonas aeruginosa (ATCC 9027), Salmonella Typhimurium (ATCC 14028), Staphylococcus aureus (ATCC 6538) e Staphylococcus epidermidis (ATCC 12228). Os espectros de FTIR-ATR forneceram informações quanto à composição de proteínas, DNA/RNA, lipídeos e carboidratos provenientes do crescimento microbiano. As identificações microbianas fornecidas pelo modelo PCA/LDA baseado no método FTIR-ATR foram compatíveis com aquelas obtidas pelos métodos microbiológicos convencionais. O método de identificação microbiana rápida por FTIR-ATR foi validado quanto à sensibilidade (93,5%), especificidade (83,3%) e limite de detecção (17-23 UFC/mL de amostra). Portanto, o MMR proposto neste trabalho pode ser usado para fornecer uma identificação rápida de contaminantes microbianos em produtos farmacêuticos. / Microbiological quality of pharmaceuticals is fundamental in ensuring efficacy and safety of medicines. Conventional methods for microbial identification in non-sterile drugs are widely used, however are time-consuming and laborious. The aim of this paper was to develop a rapid microbiological method (RMM) for identification of contaminants in pharmaceutical products using Fourier transform infrared with attenuated total reflectance spectrometry (FTIR-ATR). Principal components analysis (PCA) and linear discriminant analysis (LDA) were used to obtain a predictive model with capable to distinguish Bacillus subtilis (ATCC 6633), Candida albicans (ATCC 10231), Enterococcus faecium (ATCC 8459), Escherichia coli (ATCC 8739), Micrococcus luteus (ATCC 10240), Pseudomonas aeruginosa (ATCC 9027), Salmonella Typhimurium (ATCC 14028), Staphylococcus aureus (ATCC 6538), and Staphylococcus epidermidis (ATCC 12228) microbial growth. FTIR-ATR spectra provide information of protein, DNA/RNA, lipids, and carbohydrates constitution of microbial growth. Microbial identification provided by PCA/LDA based on FTIR-ATR method were compatible to those obtained using conventional microbiological methods. FTIR-ATR method for rapid identification of microbial contaminants in pharmaceutical products was validated by assessing the sensitivity (93.5%), specificity (83.3%), and limit of detection (17-23 CFU/mL of sample). Therefore, the RMM proposed in this work may be used to provide a rapid identification of microbial contaminants in pharmaceutical products.

Made available in DSpace on 2016-03-15T19:25:23Z (GMT). No. of bitstreams: 1 Arthur Monteiro Gabbay.pdf: 425008 bytes, checksum: 1824b9dbd4b1080b887305933b95be36 (MD5) Previous issue date: 2010-08-11 / Fundo Mackenzie de Pesquisa / Many authors consider Operational Risk as a key variable for maintaining the balance of the global financial market. The objective of this dissertation is to study the development of a Advanced Measurement Approach (AMA), specifically the Loss Distribution Approach (LDA) on a database of actual operational losses. Being more specifically, this study promotes an analysis about the results and possible limitations related to the implementation of the model. To achieve these goals, it is needed to discuss the definitions of Operational Risk, Monte Carlo Simulation and value-at-risk (VaR), considering that these concepts are crucial to the implementation of the LDA. / O risco operacional é considerado por muitos autores uma variável determinante para a manutenção do equilíbrio do mercado financeiro global. O objetivo desta dissertação é estudar o desenvolvimento de uma modelo de Abordagem de Mensuração Avançada (AMA),mais especificamente a Loss Distribution Approach (LDA), sobre um banco de dados reais de perdas operacionais. Mais especificamente este estudo promove uma análise sobre os resultados e sobre eventuais limitações relacionadas à aplicação do modelo. Para realização destes objetivos, abordam-se as definições do risco operacional, simulação de Monte Carlo e value-at-risk (VaR), haja vista que estes são conceitos cruciais para a aplicação do LDA.

On étudie le problème de la transmission de l'information à travers le canal AWGN en utilisant des réseaux. On commence par considérer des constellations infinies. Une nouvelle famille de réseaux obtenus par Construction A à partir de codes linéaires non binaires est proposée. Ces réseaux sont appelés LDA ("Low-Density Construction A") et sont caractérisés par des matrices de parité p-aires creuses, qui les mettent en relation directe avec les codes LPDC. Deux résultats sur leur possibilité d'atteindre la capacité de Poltyrev sont provés ; cela est d'abord démontré pour des poids des lignes logarithmiques des matrices de parité associées, puis pour des poids constants. Le deuxième résultat est basé sur certaines propriétés d'expansion des graphes de Tanner correspondants à ces matrices. Un autre sujet de ce travail concerne les constellations finies de réseaux. une nouvelle preuve est donnée du fait que des réseaux aléatoires obtenus par Construction A generale atteignent la capacité avec décodage de type "lattice decoding". Cela prolonge et améliore le travail de Erez et Zamir (2004), Ordentlich et Edrez (2012) Ling et Belfiore (2013). Cette preuve est basée sur les constellations de Coronoï et la multiplication par le coefficient de Wiener ("MMSE scaling") du siganl en sortie du canal. Finalement, ce résultat est adapté au cas des réseaux LDA, qui eux aussi atteignent la capacité avec le même procédé de transmission. Encore une fois, il est nécessaire d'exploiter les propriétés d'expansion des graphes de Tanner. A la fin de la dissertation, on présente un algorithme de décodage itératif et de type "message-passing" approprié au décodage des LDA en grandes dimensions. / The probleme of transmission of information over the AWGN channel using lattices is addressed. Firstly, infinite constellations are considered. A nex family of integer lattices built by means of construction A with non-binary linear condes is introduced. These lattices are called LPA (Low-Density Construction A) and are characterised by sparse p-ary parity-chedk matrices, that put them in direct relation with LPDC codes. Two results about the Poltyrev-capacity-archieving qualities of this family are proved, respectively for logarithmic row degree and constant row degree of the associated parity-check matrices. The second result is based on some expansion poperties of the Tanner graphs related to these matrices. Another topic of this work concerns finite lattice constellations. A new proff that heneral random Construction A lattices achieve capacity under lattice deconding is provided, continuing and pimproving the work of Erez and Zamir (2004), Ordentlich an Erez (2012), and Ling and Belfiore (2013). This proof is based on Voronoi lattice constellations and MMSE scaling of the channel output. Finally, this approach is adapted to the LDA case abd ut us scgiwn tgat LDA lattices achive capacity with the ame transmission scheme, too. Once again, it is necessary to exploit the expansion properties of the Tanner graphs. At he end of the dissertation, an iterative message-passing algorithm suitable for decoding LDA lattices in high dimensions is presented.