Involving customers in the creation and design process of new products and services has been dis-cussed in practice and research since the early 1980’s. As one of the first researchers, von Hippel (1986) shed light on the concept of Lead Users, a group of users who are able to provide most accu-rate data on future needs for organizations. Subsequently, many scholars emphasized different areas of contribution for customers and how they provide assistance to the process of innovation. First of all, customers may contribute to product innovation (Cooper & Kleinschmidt, 1987; Driessen & Hillebrand, 2013; Füller & Matzler, 2007; Gruner & Homburg, 2000; Sawhney, Verona, & Prandelli, 2005; Snow, Fjeldstad, Lettl, & Miles, 2011; Yang & Rui, 2009) and service innovation (Abecassis-Moedas, Ben Mahmoud-Jouini, Dell’Era, Manceau, & Verganti, 2012; Alam, 2002; Chesbrough, 2011; Larbig-Wüst, 2010; Magnusson, 2003; Paton & Mclaughlin, 2008; Shang, Lin, & Wu, 2009; Silpakit & Fisk, 1985), e.g., by co-creating values (Prahalad & Ramaswamy, 2004), such as concepts or designs as well as reviewing and testing them throughout the stages of the process of innovation. From the customers’ point of view, being involved in innovation processes and becoming a part of the organ-ization is a desire of an increasing number of them. Customers are demanding more individual and more tailored products. They are increasingly knowledgeable and capable of designing and produc-ing their own products and services. Due to the fact that their influence on product development is positively related to the quality of the new product (Sethi, 2000), more and more organizations appreciate them as innovation actors and are willing to pay them for their input. Today, customers are not only involved in the qualification of products (Callon, Méadel, & Rabeharisoa, 2002; Callon & Muniesa, 2005; Grabher, Ibert, & Flohr, 2009) but also allowed to customize and evaluate them on the path to innovation (Franke & Piller, 2004; Piller & Walcher, 2006; von Hippel & Katz, 2002; von Hippel, 2001). Moreover, there is an abundance of studies that stress the customers’ influence on effectiveness (de Luca & Atuahene-Gima, 2007; Kleinschmidt & Cooper, 1991; Kristensson, Matthing, & Johansson, 2008; Still, Huhtamäki, Isomursu, Lahti, & Koskela-Huotari, 2012) and risk (Bayer & Maier, 2006; Enkel, Kausch, & Gassmann, 2005; Enkel, Perez-Freije, & Gassmann, 2005). While the latter comprises the risk of customer integration as well as the customers’ influence on market risks, e.g., during new product development, studies on effectiveness are mostly concerned with customer-orientation and products/services in line with customers’ expectations (Atuahene-Gima, 1996, 2003; Fuchs & Schreier, 2011). The accompanying change in understanding became known as open innovation (OI; first coined by Chesbrough in 2003) and represents a paradigm shift, where organizations switch their focus from internally generated innovation (i.e., ideation, in-house R&D, etc.) toward external knowledge and open innovation processes, thus, allowing them to integrate external ideas and actors, i.e. custom-ers (Chesbrough, 2006) and other external stakeholders (Laursen & Salter, 2006). Since then, OI has been identified as a success factor for increasing customer satisfaction (Füller, Hutter, & Faullant, 2011; Greer & Lei, 2012) and growing revenues (Faems, De Visser, Andries, & van Looy, 2010; Mette, Moser, & Fridgen, 2013; Spithoven, Frantzen, & Clarysse, 2010). In addition to that, by open-ing their doors to external experts and knowledge workers (Kang & Kang, 2009), organizations cope with shorter innovation cycles, rising R&D costs, and the shortage of resources (Gassmann & Enkel, 2004). Parallel to the paradigm shift in innovation, another shift has taken place in information and com-munication technologies (Kietzmann, Hermkens, McCarthy, & Silvestre, 2011). Only a few years ago, when customer integration was still very costly, companies had to fly in customers, provide facilities onsite, permanently assign employees to such activities, and incentivise each task execut-ed by customers. Today, emerging technologies (subsumed under the term ‘social software’) help integrating customers or other external stakeholders, who are increasingly familiar with the such technologies from personal usage experience (Cook, 2008), and grant them access from all over the world in a 24/7 fashion. Examples include blogging tools, social networking systems, or wikis. These technologies help organizations to access customer knowledge, facilitate the collaboration with customers (Culnan, McHugh, & Zubillaga, 2010; Piller & Vossen, 2012) at reduced costs and allow them to address a much larger audience (Kaplan & Haenlein, 2010). On the other hand, customers can now express their needs in a more direct way to organizations. However, each technology or application category may present a completely different benefit to the process of innovation or parts of it and, thus, the innovation itself. Reflecting these developments, organizations need to know two things: how can they exploit the customers’ knowledge for innovation purposes and how may the implementation of social soft-ware support this. Hence, this research addresses the integration of customers in organizational innovation, i.e. new product development. It addresses how and why firms activate customers for innovation and which contribution customers provide to the process of innovation. Additionally, it investigates which tasks customers may take over in open innovations projects and which strategies organiza-tions may choose to do so. It also addresses which social software application supports each task best and how organizations may select the most suitable application out of a rapidly growing num-ber of alternatives. The nature of this research is recommendatory and aims at designing a solution for organizations that are interested in the potential contribution of customers during innovation, already involve customers in innovation tasks or plan to do so. Following the recommendations of this research should result in a more effective organizational exploitation of customer knowledge and their workforce and, thus, a value added to innovation and the outcomes of the process of innovation, e.g., a product that better fits the customers’ expectations and demands or consequently a better adoption of the product by the customer.

[pt] A área de química do estado sólido ocupa uma posição cada vez mais importante nas atividades de pesquisa e desenvolvimento farmacêuticas. A compreensão das propriedades do estado sólido de um insumo farmacêutico ativo (IFA) mostra-se crítica no desenvolvimento de formulações em função de seus impactos na biodisponibilidade e solubilidade dos fármacos, sendo essencial para garantir o benefício terapêutico, otimizar o desenvolvimento e garantir a proteção da propriedade intelectual. Esta tese investiga indicadores científicos e tecnológicos na área de química do estado sólido utilizando ferramentas de ciência dos dados a partir de publicações científicas e depósitos de patentes, visando contribuir para o aumento da competitividade da indústria farmoquímica e farmacêutica brasileira e de outros mercados emergentes. A partir da utilização de ferramentas de ciência dos dados é proposta uma metodologia baseada em técnicas de text mining associadas a relações fuzzy. Essa metodologia de identificação de competências específicas aplicada na área de química do estado sólido tem como estudo de caso a descoberta de uma nova forma polimórfica para o IFA acetato de dexametasona. Os resultados revelam que existem competências científicas em química do estado sólido no Brasil. Contudo, quando comparada com a interação universidade-empresa mundial, a indústria farmoquimica e farmacêutica local perde em estágio de competitividade e desenvolvimento. Por outro lado, os resultados demonstram a robustez da metodologia e sua capacidade de identificar pesquisadores em área específicas, oferecendo soluções para apoio a tomada de decisão e identificação de pesquisadores relevantes para o desenvolvimento do setor farmoquímico e farmacêutico. / [en] The solid-state chemistry area has received increased attention in the pharmaceutical research and development activities. The comprehension of the solid-state properties of an active pharmaceutical ingredient (API) is critical in the development of formulations due to their impact on the bioavailability and solubility of the final drug, being essential to ensure therapeutic benefit, optimize development and allow a proper intellectual property protection. This research investigates science and technology indicators in the solid-state chemistry area using data science tools applied to scientific publications and patent documents, aiming to contribute to the increase of the competitiveness of the pharmaceutical industry in Brazil and in other emerging markets. Through data science tools, a methodology based on text mining techniques associated to fuzzy relations is proposed. This methodology for identifying specific competencies is applied in the solid-state chemistry area exploring a case study of the discovery of a new polymorphic form of the API dexamethasone acetate. The results reveal the existence of scientific competencies in solid-state chemistry in Brazil. However, when compared to the global university-company interaction, the local pharmaceutical industry shows a lower stage of competitiveness and development. On the other hand, the results indicates the robustness of the methodology and its ability to identify researchers in specific areas, offering solutions to support the decision making and identification of researchers relevant to the development of the pharmaceutical sector.

Notre travail de recherche explore de manière comparée le développement du neuromarketing aux Etats-Unis et en France. Nous commençons par analyser la littérature sur le neuromarketing. Nous utilisons comme cadre théorique et méthodologique l’Actor Network Theory (ANT) ou Théorie de l’Acteur-Réseau (dans le sillage des travaux de Bruno Latour et Michel Callon). Nous montrons ainsi comment des actants « humains et non-humains »: acteurs-réseaux, traces (publications) et controverses forment les piliers d’une nouvelle discipline telle que le neuromarketing. Notre approche hybride « qualitative-quantitative », nous permet de construire une méthodologie appliquée de l’ANT: analyse bibliométrique (Publish Or Perish), text mining, clustering et analyse sémantique de la littérature scientifique et web du neuromarketing. A partir de ces résultats, nous construisons des cartographies, sous forme de graphes en réseau (Gephi) qui révèlent les interrelations et les associations entre acteurs, traces et controverses autour du neuromarketing. / Our research explores the comparative development of neuromarketing between the United States and France. We start by analyzing the literature on neuromarketing. We use as theoretical and methodological framework the Actor Network Theory (ANT) (in the wake of the work of Bruno Latour and Michel Callon). We show how “human and non-human” entities (“actants”): actor-network, traces (publications) and controversies form the pillars of a new discipline such as the neuromarketing. Our hybrid approach “qualitative-quantitative” allows us to build an applied methodology of the ANT: bibliometric analysis (Publish Or Perish), text mining, clustering and semantic analysis of the scientific literature and web of the neuromarketing. From these results, we build data visualizations, mapping of network graphs (Gephi) that reveal the interrelations and associations between actors, traces and controversies about neuromarketing.

Involving customers in the creation and design process of new products and services has been dis-cussed in practice and research since the early 1980’s. As one of the first researchers, von Hippel (1986) shed light on the concept of Lead Users, a group of users who are able to provide most accu-rate data on future needs for organizations. Subsequently, many scholars emphasized different areas of contribution for customers and how they provide assistance to the process of innovation. First of all, customers may contribute to product innovation (Cooper & Kleinschmidt, 1987; Driessen & Hillebrand, 2013; Füller & Matzler, 2007; Gruner & Homburg, 2000; Sawhney, Verona, & Prandelli, 2005; Snow, Fjeldstad, Lettl, & Miles, 2011; Yang & Rui, 2009) and service innovation (Abecassis-Moedas, Ben Mahmoud-Jouini, Dell’Era, Manceau, & Verganti, 2012; Alam, 2002; Chesbrough, 2011; Larbig-Wüst, 2010; Magnusson, 2003; Paton & Mclaughlin, 2008; Shang, Lin, & Wu, 2009; Silpakit & Fisk, 1985), e.g., by co-creating values (Prahalad & Ramaswamy, 2004), such as concepts or designs as well as reviewing and testing them throughout the stages of the process of innovation. From the customers’ point of view, being involved in innovation processes and becoming a part of the organ-ization is a desire of an increasing number of them. Customers are demanding more individual and more tailored products. They are increasingly knowledgeable and capable of designing and produc-ing their own products and services. Due to the fact that their influence on product development is positively related to the quality of the new product (Sethi, 2000), more and more organizations appreciate them as innovation actors and are willing to pay them for their input. Today, customers are not only involved in the qualification of products (Callon, Méadel, & Rabeharisoa, 2002; Callon & Muniesa, 2005; Grabher, Ibert, & Flohr, 2009) but also allowed to customize and evaluate them on the path to innovation (Franke & Piller, 2004; Piller & Walcher, 2006; von Hippel & Katz, 2002; von Hippel, 2001). Moreover, there is an abundance of studies that stress the customers’ influence on effectiveness (de Luca & Atuahene-Gima, 2007; Kleinschmidt & Cooper, 1991; Kristensson, Matthing, & Johansson, 2008; Still, Huhtamäki, Isomursu, Lahti, & Koskela-Huotari, 2012) and risk (Bayer & Maier, 2006; Enkel, Kausch, & Gassmann, 2005; Enkel, Perez-Freije, & Gassmann, 2005). While the latter comprises the risk of customer integration as well as the customers’ influence on market risks, e.g., during new product development, studies on effectiveness are mostly concerned with customer-orientation and products/services in line with customers’ expectations (Atuahene-Gima, 1996, 2003; Fuchs & Schreier, 2011). The accompanying change in understanding became known as open innovation (OI; first coined by Chesbrough in 2003) and represents a paradigm shift, where organizations switch their focus from internally generated innovation (i.e., ideation, in-house R&D, etc.) toward external knowledge and open innovation processes, thus, allowing them to integrate external ideas and actors, i.e. custom-ers (Chesbrough, 2006) and other external stakeholders (Laursen & Salter, 2006). Since then, OI has been identified as a success factor for increasing customer satisfaction (Füller, Hutter, & Faullant, 2011; Greer & Lei, 2012) and growing revenues (Faems, De Visser, Andries, & van Looy, 2010; Mette, Moser, & Fridgen, 2013; Spithoven, Frantzen, & Clarysse, 2010). In addition to that, by open-ing their doors to external experts and knowledge workers (Kang & Kang, 2009), organizations cope with shorter innovation cycles, rising R&D costs, and the shortage of resources (Gassmann & Enkel, 2004). Parallel to the paradigm shift in innovation, another shift has taken place in information and com-munication technologies (Kietzmann, Hermkens, McCarthy, & Silvestre, 2011). Only a few years ago, when customer integration was still very costly, companies had to fly in customers, provide facilities onsite, permanently assign employees to such activities, and incentivise each task execut-ed by customers. Today, emerging technologies (subsumed under the term ‘social software’) help integrating customers or other external stakeholders, who are increasingly familiar with the such technologies from personal usage experience (Cook, 2008), and grant them access from all over the world in a 24/7 fashion. Examples include blogging tools, social networking systems, or wikis. These technologies help organizations to access customer knowledge, facilitate the collaboration with customers (Culnan, McHugh, & Zubillaga, 2010; Piller & Vossen, 2012) at reduced costs and allow them to address a much larger audience (Kaplan & Haenlein, 2010). On the other hand, customers can now express their needs in a more direct way to organizations. However, each technology or application category may present a completely different benefit to the process of innovation or parts of it and, thus, the innovation itself. Reflecting these developments, organizations need to know two things: how can they exploit the customers’ knowledge for innovation purposes and how may the implementation of social soft-ware support this. Hence, this research addresses the integration of customers in organizational innovation, i.e. new product development. It addresses how and why firms activate customers for innovation and which contribution customers provide to the process of innovation. Additionally, it investigates which tasks customers may take over in open innovations projects and which strategies organiza-tions may choose to do so. It also addresses which social software application supports each task best and how organizations may select the most suitable application out of a rapidly growing num-ber of alternatives. The nature of this research is recommendatory and aims at designing a solution for organizations that are interested in the potential contribution of customers during innovation, already involve customers in innovation tasks or plan to do so. Following the recommendations of this research should result in a more effective organizational exploitation of customer knowledge and their workforce and, thus, a value added to innovation and the outcomes of the process of innovation, e.g., a product that better fits the customers’ expectations and demands or consequently a better adoption of the product by the customer.:1 Introduction 2 Theoretical foundation 3 Research areas and focal points 4 Research aims and questions 5 Methods 6 Findings 7 Conclusion References Essay 1: The Role of External Knowledge in Open Innovation – A Systematic Review of Literature Essay 2: External Knowledge in Organisational Innovation – Toward an Integration Concept Essay 3: Idea Mining – Text Mining Supported Knowledge Management for Innovation Purposes Essay 4: How do Tasks and Technology fit? – Bringing Order to the Open Innovation Chaos

Die Untersuchung von Beziehungen zwischen biomedizinischen Entitäten bildet einen Eckpfeiler der modernen Medizin. Angesichts der rasanten Zunahme der Forschungsliteratur wird es jedoch zunehmend schwieriger, durch bloßes Lesen umfassende Informationen über bestimmte Entitäten und deren Beziehungen zu gewinnen. Text-Mining Ansätze versuchen, die Verarbeitung dieser riesigen Datenmengen mit Hilfe von Maschinellen Lernen zu erleichtern. Wir tragen zu dieser Forschung bei indem wir Methoden zum Erlernen von Entitäts- und Textrepräsentationen auf Basis großer Publikations- und Wissensdatenbanken entwickeln. Als erstes schlagen wir zwei neuartige Ansätze zur Relationsextraktion vor, die Techniken des Representation Learnings nutzen, um umfassende Modelle biomedizinischer Entitäten und Entitätspaaren zu lernen. Diese Modelle lernen Vektorrepräsentationen, indem sie alle PubMed-Artikel berücksichtigen, die eine bestimmte Entität oder ein Entitätspaar erwähnen. Wir verwenden diese Vektoren als Eingabe für ein neuronales Netzwerk, um Relationen global zu klassifizieren, d. h. die Vorhersagen basieren auf dem gesamten Korpus und nicht auf einzelnen Sätzen oder Artikeln wie in konventionellen Ansätzen. In unserem zweiten Beitrag untersuchen wir die Auswirkungen multimodaler Entitätsinformationen auf die Vorhersage von Relationen mithilfe von Knowledge Graph Embedding Methoden. In unserer Studie erweitern wir bestehende Modelle, indem wir Wissensgraphen mit multimodalen Informationen anreichern. Ferner schlagen wir ein allgemeines Framework für die Integration dieser Informationen in den Lernprozess für Entitätsrepräsentationen vor. In unserem dritten Beitrag erweitern wir Sprachmodelle mit zusätzlichen Entitätsinformationen für die Identifikation von Relationen in Texten. Wir führen eine umfangreiche Evaluation durch, welche die Leistung solcher Modelle in mehreren Szenarien erfasst und damit eine umfassende, jedoch bisher fehlende, Bewertung solcher Modelle liefert. / With the rapid growth of biomedical literature, obtaining comprehensive information regarding particular biomedical entities and relations by only reading is becoming increasingly difficult. Text mining approaches seek to facilitate processing these vast amounts of text using machine learning. This renders effective and efficient encoding of all relevant information regarding specific entities as one central challenge in these approaches. In this thesis, we contribute to this research by developing machine learning methods for learning entity and text representations based on large-scale publication repositories and diverse information from in-domain knowledge bases. First, we propose two novel relation extraction approaches that use representation learning techniques to create comprehensive models of entities or entity pairs. These models learn low-dimensional embeddings by considering all publications from PubMed mentioning a specific entity or pair of entities. We use these embeddings as input for a neural network to classify relations globally, i.e., predictions are based on the entire corpus, not on single sentences or articles as in prior art. In our second contribution, we investigate the impact of multi-modal entity information for biomedical link prediction using knowledge graph embedding methods (KGEM). Our study enhances existing KGEMs by augmenting biomedical knowledge graphs with multi-modal entity information from in-domain databases. We propose a general framework for integrating this information into the KGEM entity representation learning process. In our third contribution, we augment pre-trained language models (PLM) with additional context information to identify interactions described in scientific texts. We perform an extensive benchmark that assesses the performance of such models across a wide range of biomedical relation scenarios, providing a comprehensive, but so far missing, evaluation of knowledge-augmented PLM-based extraction models.

現今資料大量且快速數位化的時代，各領域對資訊探勘分析技術越趨倚重。而在數位人文中領域中從2009年「數位典藏與數位人文國際研討會」開始，此議題逐漸受到重視，主要目的為將數位文物結合資訊分析與圖像化輔助，透過不同層面的詮釋建構出更完整的文物資訊。 本研究建構一個針對各種中文語料分析的工具，藉由latent semantic analysis、pointwise mutual information、Person’s chi-squared test、typed dependencies distance、word2vec、Gibbs sampling for latent Dirichlet allocation等計算語料中關鍵詞彙關聯強度的方法，並結合分群方法找出可能的主題，最後擷取符合分群結果的相關句子予以輔助人文學者分析詮釋。透過提供各種觀察語料的面向，進而提升語料相關研究學者的效率。 我們利用《人民日報》、《新青年》、《聯合報》、《中國時報》作為實驗與測試的中文語料。且將《新青年》藉由此套工具分析後的結果提供給專業人文學者，做為分析詮釋的參考資訊與佐證依據，並在「2015年數位典藏與數位人文國際研討會」中發表論文。目前我們透過各種中文語料評估工具的效能，且在未來將公開此套工具提供給更多學者使用，節省對於語料分析的時間。 / In recent years, a wide variety of text documents have been transformed into digital format. Hence, using data mining techniques to analyze data is becoming more and more popular in many research fields. The digital humanities gradually have taken seriously since "International Conference of Digital Archives and Digital Humanities" began in 2009. The main purpose of the digital heritage combined with information analysis and visualization could improve the effectiveness of cultural information through different levels of interpretation. In this study, we construct a set of tools for Chinese text mining, calculating associated strengths of collocations work through latent semantic analysis, pointwise mutual information, Person’s chi-squared test, typed dependencies distance, word2vec, and Gibbs sampling for latent Dirichlet allocation etc. The tools employ clustering method to identify the possible topics, meanwhile, the tools will extract the relevant statements according to the clustering results. These clustering and relevant statements contribute and improve the efficiency of humanities scholars’ analysis through providing a variety of observations about the corpora. At the experimental stage of this study, we considered the "People's Daily", "New Youth", "United Daily News", and "China Times" as as the corpora for testing. Among the research, humanities scholars analyzed the "New Youth" by the tools and published a paper in the "2015 International Conference of Digital Archives and Digital Humanities". Currently, we assess the effectiveness of the tools through a variety of Chinese corpora. In the future, we will make the tools freely available on the Internet for Chinese text mining. We hope these time-saving tools can assist in humanities scholars’ study of Chinese corpora.

近年來傳統線上購物正面臨著一連串的市場困境，如削價競爭、廉價品競爭等，因此導致銷售量之成長趨緩，反觀跨境線上購物卻出現了蓬勃發展的態勢，因而讓跨境線上購物成為驅動經濟活動與國際貿易的新引擎。另一方面，由於跨境線上購物的情境複雜性遠高於傳統的境內線上購物，業者們欲開發一海外新市場，必須先了解該地消費者行為與其購買決策過程後，才能制定出好的商業策略，並且進一步將產品導向的服務轉化成為以顧客導向的服務，才有機會為傳統線上購物之困境另闢生機。因此，引取並了解消費者所體認的內在價值是經營跨境線上購物最重要的成功因素。 本研究將試圖將傳統境內線上購物研究擴展到跨境線上購物議題，藉由文字探勘(Text Mining)分析、語意情感分析與 k-means 分群演算法，挖掘出消費者對於所購買商品之評論的常見內容型態與所購買商品之類別，並試圖找出跨境網購平台上各項因素及商品評論對於產品銷售量間之關連性，提供未來研究者及跨境網購平台業者決策之依據。 / While online shopping websites are facing the difficulties of price and low-quality competition, cross-border online shopping is on a vigorous development trend, showing that cross-border online shopping is an important trend of online shopping field. Due to the complexity of cross-border online shopping is much higher than the traditional domestic online shopping, so understanding the value of cross-border online shopping consumers is the most important success factors. Companies want to develop new markets abroad, must understand the local consumer’s behaviour and their decision-making process in order to make good business strategies. This study uses text mining analytic technology, semantic analysis techniques, and k-means clustering algorithm to identify characteristics of consumers’ reviews and the common categories of goods they purchased. After getting the reason why consumers use cross-border online shopping service and what values they got in this service. Researcher can predict and analyse the evolution and development of cross-border online shopping, provide reference for future online shopping academic studies and online shopping industry’s decision-making.

網際網路時代資訊接收的便利性，使得大眾容易接收到媒體所發布的媒體資訊，而這些資料具含的意見詞彙間接反應出群眾對特定主題的情緒傾向。在針對房地產的媒體當中，當特定區域的房地產市場具有良好的發展空間而成為交易熱區時，這些針對特定區域且帶含情緒的房市篇章報導或其他影響房市之相關新聞以及廣告往往會影響我們的購屋決策。 本研究將以桃園市及台中市-兩個近五年來台灣房市較為熱門的區域作為研究區域進行分析及研究，期望找出在短期時間新聞輿情及廣告和房市交易價量的相關性以及會影響該房地產市場之因素。首先蒐集桃園市及台中市的實價登錄的房地產交易資料以及廣告後，運用文字探勘分析房市整體輿情與兩都市房地產價量之關聯性，再將新聞分群後找出特徵詞，個別建立時間序列來了解各種情緒及房地產價量的共同移動性，並結合廣告投入量找出房地產市場價量以及影響因素的領先關係。並透過自建的類神經網路模型建立針對桃園市和台中市的交易量預測模型以及針對特定房市熱門區域-青埔和七期的交易量預測模型，並透過計算輸入變數的權重總和來判別新聞情緒對於房地產成交價量的影響程度。 研究首先提供了對於新聞情緒的分類包含區域經濟情緒、區域社會情緒、區域環境情緒、區域政治情緒、稅制情緒、選舉情緒。接著進行時間序列分析指出總情緒序列與成交量的時間序列相關係數都有高於70%以上，桃園市成交量與桃園市情緒的相關係數為0.73，台中市成交量與台中市情緒的相關係數為0.81，皆呈現高度正相關，顯示桃園及台中的房市交易量與情緒現存在高度相關性。在特定新聞類別當中，透過兩個城市的相關係數比對顯示稅制新聞情緒，區域環境相關情緒，區域社會相關情緒，以上三個情緒跟房市的交易量共同移動較為明顯，相關係數皆在0.5左右甚至以上，可見這些類別的新聞能夠適時反映大眾對於特定區域的房地產的看好及看壞。在此階段也透過領先指標驗證了情緒以及廣告是會領先房市交易量，桃園以及台中兩個區域都有情緒領先交易量一個月的現象。針對特定區域的交易量研究包含青埔特區及七期重劃區，也發現到兩地的交易量高峰前一至兩個月都有一波廣告的高峰。 而在類神經網路模型方面的研究結果能夠良好地預測漲跌趨勢，利用桃園資料進行訓練並以台中資料做為測試的模型在19次的漲跌中預測出17次，而將百分之七十的桃園及台中混合資料進行訓練並其餘百分之三十做為測試的模型結果也成功在14次漲跌中預測出10次，顯示模型效果預測能力良好，並透過將輸入權重加總的方式來衡量各輸入變數的影響程度，研究結果指出總情緒，稅制情緒量，區域環境情緒量與兩地房地產市場交易量最有關聯且影響最重。最後利用時間序列得知廣告高峰會領先總交易高峰一至兩個月的特性，利用從2012年10月至2016年2月的青埔特區資料及2012年10月至2013年12月的七期重劃區資料混合進行訓練並以2014年1月至2016年2月七期重劃區資料做為測試資料的模型能夠有效在兩年內預測中三次交易高峰，顯示該模型能透過預測出下一期的廣告投入量做為中介變數進而推估出交易量高峰的時間透過此模型可在未來應用於相關政策投入市場後對市場交易量的影響，也能夠快速有效的得到預測結果，而在針對特定市場我們也可以透過預測廣告以及運用廣告為交易量的領先特性來了解在近期何時會有交易量高峰，如能配合了解市場輿情脈絡，可為房屋仲介以及建商在更精確的時間點投放廣告時機點達到廣告的最大效益。

股價的漲跌變化是由於證券市場中眾多不同投資人及其投資決策後所產生的結果。然而，影響股價變動的因素眾多且複雜，新聞也屬於其中一種，新聞事件不但是投資人用來得知該股票上市公司的相關營運資訊的主要媒介，同時也是影響投資人決定或變更其股票投資策略的主要因素之一。本研究提出以新聞文件做為股價漲跌預測系統的基礎架構，透過文字探勘技術及分類技術來建置出能預測當日個股收盤股價漲跌趨勢之系統。 本研究共提出三種分類模型，分別是簡易貝氏模型、k最近鄰居模型以及混合模型，並設計了三組實驗，分別是分類器效能的比較、新聞樣本資料深度的比較、以及新聞樣本資料廣度的比較來檢驗系統的預測效能。實驗結果顯示，本研究所提出的分類模型可以有效改善相關研究中整體正確率高但各個類別的預測效能卻差異甚大的情況。而對於影響投資人獲利與否的關鍵類別"漲"及類別"跌"的平均預測效能上，本研究所提出的這三種分類模型亦同時具有良好的成效，可以做為投資人進行投資決策時的有效參考依據。 / Stocks' closing price levels can provide hints about investors' aggregate demands and aggregate supplies in the stock trading markets. If the level of a stock's closing price is higher than its previous closing price, it indicates that the aggregate demand is stronger than the aggregate supply in this trading day. Otherwise, the aggregate demand is weaker than the aggregate supply. It would be profitable if we can predict the individual stock's closing price level. For example, in case that one stock's current price is lower than its previous closing price. We can do the proper strategies(buy or sell) to gain profit if we can predict the stock's closing price level correctly in advance. In this thesis, we propose and evaluate three models for predicting individual stock's closing price in the Taiwan stock market. These models include a naïve Bayes model, a k-nearest neighbors model, and a hybrid model. Experimental results show the proposed methods perform better than the NewsCATS system for the "UP" and "DOWN" categories.

投資人在投資決策之過程中，所分析之資料可分為財務性與非財務性資訊兩大類，然而受限於傳統財務資料格式之不一致，可能需花費額外之財力與物力來處理，甚至浪費精力於資料的重新輸入。另一方面，非財務資訊在投資決策過程中日益重要，但其龐大的資訊揭露量卻往往徒增投資人閱讀與搜尋上之不便，甚至降低了可閱讀性。 有鑑於上述兩大投資分析不便之處，本研究運用文字探勘(Text mining)技術，嘗試處理股東會年報中與企業策略相關之非財務性資訊，以協助閱讀者有效率地分析、整理這些半結構化，甚至是非結構化文字資訊。另一方面，本研究利用可延伸企業報導語言(eXtensible Business Reporting Language, XBRL)不受軟體平台限制，可於網路上自由下載流通等特性，作為財務資訊之資料來源，同時建立一種新的分析模式，透過連結機制之設計以連接非財務性與財務性資訊，並運用ROMC系統分析法與雛型系統設計法完成本企業策略分析決策支援系統，希冀能協助投資人能於短時間內瞭解並印證標的公司之產業發展與競爭策略，提升決策品質。 / There are two main data types in investment decision process: financial and non-financial. Because the inconsistent of data type in traditional financial data, investors may have more additional costs to solve this problem. In addition, non-financial data become more and more important in investment decision process, but huge amount of non-financial disclosure may reduce the readability and increase the difficulty of searching. To solve the above problems, we try to use text mining technology to handle the semi-structured or unstructured non-financial data related to business strategies in the annual reports of public companies effectively and efficiently. In addition, we use XBRL (eXtensible Business Reporting Language) to be our financial data resources because of its interoperability and re-usability. We also develop a new analytic method to link financial and non-financial data together. Finally, we use two system methodologies: R.O.M.C. and prototyping to design and build our business strategy analysis decision support system in order to help investors understand and prove strategies in companies, and improve the decision quality which they make.