Text documents are generally accompanied by non-textual information, such as authors, dates, publication sources, and, increasingly, automatically recognized named entities. Work in text analysis has often involved predicting these non-text values based on text data for tasks such as document classification and author identification. This thesis considers the opposite problem: predicting the textual content of documents based on non-text data. In this work I study several regression-based methods for estimating the influence of specific metadata elements in determining the content of text documents. Such topic regression methods allow users of document collections to test hypotheses about the underlying environments that produced those documents.

There are multiple sides to every story, and while statistical topic models have been highly successful at topically summarizing the stories in corpora of text documents, they do not explicitly address the issue of learning the different sides, the viewpoints, expressed in the documents. In this paper, we show how these viewpoints can be learned completely unsupervised and represented in a human interpretable form. We use a novel approach of applying CorrLDA2 for this purpose, which learns topic-viewpoint relations that can be used to form groups of topics, where each group represents a viewpoint. A corpus of documents about the Israeli-Palestinian conflict is then used to demonstrate how a Palestinian and an Israeli viewpoint can be learned. By leveraging the magnitudes and signs of the feature weights of a linear SVM, we introduce a principled method to evaluate associations between topics and viewpoints. With this, we demonstrate, both quantitatively and qualitatively, that the learned topic groups are contextually coherent, and form consistently correct topic-viewpoint associations. / I detta kandidatexamensarbete demonstrerar vi hur åsikter som uttrycks i artiklar om aktuella händelser kan modeleras med en oövervakad inlärningsmetod. Vi anpassar CorrLDA2-modellen för detta syfte, som kan lära sig vilka ämnen som diskuteras i en samling av textdokument, vilka åsikter som uttrycks, samt relationer mellan ämnen och åsikter. Med hjälp av dessa relationer kan vi sedan bilda grupper av ämnen, där varje grupp är associerad med en åsikt. Detta skapar en representation av åsikter som är tolkbar för människor. Vi demonstrerar detta med hjälp av en samling av dokument som handlar om Israel-Palestinakonflikten, genom att bilda en grupp av ämnen som representerar den palestinska åsikten, samt en grupp som representerar den isrealiska åsikten. Vi introducerar sedan en ny evalueringsmetod, som använder sig av magnituden samt tecknen på attributsvikter från en linjär SVM. Med hjälp av detta visar vi, både kvantitativt och kvalitativt, att de inlärda relationerna mellan ämenen och åsikter bildar sammanhängande ämnesgrupper, samt konsikvent korrekta associationer mellan ämnen och åsikter. / <p>This is the second time I am submitting my thesis here on DiVa.</p><p>I didn't attach the actual thesis document (i.e. the pdf file) last time because we were submitting on for publication in a scientific conference and I wanted to respect the double blind review process and not publish anything before.</p><p>Now, I want to publish the thesis document here on DiVa.</p>

This thesis aims to deepen the relationship between the different forms of proximity that emerge between economic actors and the consequent influence on their innovative capacity. Over the years, this topic has generated a great deal of attention in conference proceedings and scientific publications. The first step to deepen the understanding of this amount of knowledge was to identify a suitable methodology. In so do- ing, the recent advances of the Machine Learning community – particularly Natural Language Processing academics - have offered interesting insights. In particular, "Topic Modeling" was identified as a suitable methodology to bring out latent semantic structures. Therefore, the first chapter tries to study how this methodology has been implemented in the social sciences and, in particular, in management. The contribution offered is a rationalization of the achievable goals and their relationship with evaluation practices. Once clarified how to use this algorithm, the second chapter studied the relationship between proximity and innovation. Using an unsupervised machine learning technique, the research attempts to identify thematic management cores in a multifocal literature such as proximity. Together with a qualitative analysis, the study attempts to bring out the theoretical and empirical contributions offered to the management community. Once the theoretical and empirical expectations have been clarified, the third chapter introduces a strategic theme, namely the business model. This section proposes a mediating effect of the business model concerning the central relationship between proximity and innovation. After a theoretical introduction, the conceptual model is studied with an exploratory approach. Without any presumption of generalizability and completeness, a novel analytical key is offered to open further debate in the community of proximity.

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The rise of big data, especially social media data (e.g., Twitter, Facebook, Youtube), gives new opportunities to the understanding of human behavior. Consequently, novel computing methods for mining patterns in social media data are therefore desired. Through applying these approaches, it has become possible to aggregate public available data to capture triggers underlying events, detect on-going trends, and forecast future happenings. This thesis focuses on developing methods for social media analysis. Specifically, five directions are proposed here: 1) semi-supervised detection for targeted-domain events, 2) topical interaction study among multiple datasets, 3) discriminative learning about the identifications for common and distinctive topics, 4) epidemics modeling for flu forecasting with simulation via signals from social media data, 5) storyline generation for massive unorganized documents. / Ph. D.

The main aim of the thesis is to investigate how discourse entities are linked with topic chaining and discourse coherence by showing that the choice and the distribution of referring expressions is correlated with the center transition patterns in the centering framework. The thesis provides an integrated interpretation in understanding the behaviour of referring expressions in discourse by considering the relation between referential choice and the local and global coherence of discourse. The thesis has three stages: (1) to provide a semantic and pragmatic perspective in a contrastive study of referring expressions in English and Japanese spontaneous dialogues, (2) to analyse the way anaphoric and deictic expressions can contribute to discourse organisation in structuring and focusing the specific discourse segment, and (3) to investigate the choice and the distribution of referring expressions in the Map Task Corpus and to clarify the way the participants collaborate to judge the most salient entity in the current discourse against their common ground. Significantly, despite the grammatical differences in the form of reference between the two languages, the ways of discourse development in both data sets show distinctive similarities in the process by which the topic entities are introduced, established, and shifted away to the subsequent topic entities. Comparing and contrasting the choice and the distribution of referring expressions of the four different transition patterns of centers, the crucial factors of their correspondent relations between English and Japanese referring expressions are shown in the findings that the topic chains of noun phrases are constructed and are treated like proper names in discourse. This can suggest that full noun phrases play a major role when the topic entity is established in the course of discourse. Since the existing centering model cannot handle the topic chain of noun phrases in the anaphoric relations in terms of the local focus of discourse, centering must be integrated with a model of global focus to account for both pronouns and full noun phrases that can be used for continuations across segment boundaries. Based on Walker’s cache model, I argue that the forms of anaphors are not always shorter, and the focus of attention is maintained by the chain of noun phrases rather than by (zero) pronouns both within a discourse segment and over discourse segment boundaries. These processes are predicted and likely to underlie other uses of language as well. The result can modify the existing perspectives that the focus of attention is normally represented by attenuated forms of reference, and full noun phrases always show focus-shift. In addition, necessary extension to the global coherence of discourse can link these anaphoric relations with the deictic expressions over discourse segment boundaries. Finally, I argue that the choice and the distribution of referring expressions in the Map Task Corpus depends on the way the participants collaborate to judge the most salient entity in the current discourse against their common ground.

Sentiment analysis aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text, and has received a rapid growth of interest in natural language processing in recent years. Probabilistic topic models, on the other hand, are capable of discovering hidden thematic structure in large archives of documents, and have been an active research area in the field of information retrieval. The work in this thesis focuses on developing topic models for automatic sentiment analysis of web data, by combining the ideas from both research domains. One noticeable issue of most previous work in sentiment analysis is that the trained classifier is domain dependent, and the labelled corpora required for training could be difficult to acquire in real world applications. Another issue is that the dependencies between sentiment/subjectivity and topics are not taken into consideration. The main contribution of this thesis is therefore the introduction of three probabilistic topic models, which address the above concerns by modelling sentiment/subjectivity and topic simultaneously. The first model is called the joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. Unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when applied to new domains, the weakly-supervised nature of JST makes it highly portable to other domains, where the only supervision information required is a domain-independent sentiment lexicon. Apart from document-level sentiment classification results, JST can also extract sentiment-bearing topics automatically, which is a distinct feature compared to the existing sentiment analysis approaches. The second model is a dynamic version of JST called the dynamic joint sentiment-topic (dJST) model. dJST respects the ordering of documents, and allows the analysis of topic and sentiment evolution of document archives that are collected over a long time span. By accounting for the historical dependencies of documents from the past epochs in the generative process, dJST gives a richer posterior topical structure than JST, and can better respond to the permutations of topic prominence. We also derive online inference procedures based on a stochastic EM algorithm for efficiently updating the model parameters. The third model is called the subjectivity detection LDA (subjLDA) model for sentence-level subjectivity detection. Two sets of latent variables were introduced in subjLDA. One is the subjectivity label for each sentence; another is the sentiment label for each word token. By viewing the subjectivity detection problem as weakly-supervised generative model learning, subjLDA significantly outperforms the baseline and is comparable to the supervised approach which relies on much larger amounts of data for training. These models have been evaluated on real world datasets, demonstrating that joint sentiment topic modelling is indeed an important and useful research area with much to offer in the way of good results.

A dissertação mapeia a formação da identidade internacional da União Europeia através da sua arquitetura de segurança internacional que tem como um dos seus nós a securitização da imigração, utilizando ferramentas metodológicas não-tradicionais para confirmar a nossa tese. A primeira parte do trabalho é um marco teórico: discutimos a virada linguística nas relações internacionais para entender a intersubjetividade entre pesquisador e objeto, de modo que nós escolhemos reflexividade como a nossa abordagem metodológica; em seguida, discutimos as escolas europeias em segurança internacional do pós-guerra fria, como a Escola de Copenhague, Escola Crítica de Gales e Escola de Paris, apresentando conceitos e objetos estudados por especialistas que nos são caros para entender nosso estudo e colocar nossa pesquisa dentro de sua comunidade epistêmica; finalmente, discutimos e incorporamos conceitos e abordagens da Teoria do Discurso (estudos de Ernesto Laclau, Chantal Mouffe e Escola de Essex) para fazer uma construção cronológica e geodiscursiva da região euromediterrânea. Na segunda parte, reconstruímos histórica e institucionalmente a arquitetura europeia de segurança internacional do pós-guerra a hoje vis-à-vis com suas políticas de migração notando suas correlações, também com foco na análise detalhada dos principais documentos oficiais de segurança. A parte quantitativa final (e nossa contribuição original) procura confirmar a causalidade do link segurança-imigração na arquitetura europeia; para isso, utilizamo-nos da linguística computacional para análise semântica semi-automatizada, mais especificamente Topic Model; analisamos cerca de 20.000 documentos oficiais de segurança da União Europeia para indicar estatísticas, agentes, instituições, agendas e discursos que confirmam nossa tese. / The dissertation maps the formation of the international identity of European Union through its international security architecture that has as one of its nodes the securitization of immigration, using non-traditional methodological tools to confirm our thesis. The first part of the work is a theoretical framework: we discuss the linguistic turn in international relations to understand the intersubjectivity between researcher and object so we choose Reflexivity as our methodological approach; then we discuss the European schools in international security from post-cold war such as the Copenhagen School, Wales Critical School and Paris School, presenting concepts and objects studied by experts who are dear to us to understand our study and place our research within its epistemic community; Finally, we discuss and incorporate concepts and approaches from Discourse Theory (studies from Ernesto Laclau, Chantal Mouffe and Essex School) to make a chronological geodiscursive construction of the euromediterranean region. In the second part, we reconstruct historically and institutionally the European international security architecture from post-war till today vis-à-vis with its migration policies and noting their correlations, also focusing on detailed analysis of the main official security documents. A final quantitative section (and our original contribution) seeks to confirm the causality of the security-immigration link in European architecture; for this we use computational linguistics for semi-automated semantic analysis, more specifically Topic Model; We analyze around 20,000 official security documents from European Union to indicate statistics, agents, institutions, agendas and speeches which confirm our thesis.

by Gu Gang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 135-142). / Abstract also in Chinese. / ABSTRACT --- p.v / Chapter CHAPTER 1 --- INTRODUCTION --- p.1 / Chapter CHAPTER 2 --- THE THEORETICAL FRAMEWORK --- p.7 / Chapter 2.1 --- Grammaticality and Acceptability --- p.7 / Chapter 2.2 --- The X-bar Theory --- p.9 / Chapter 2.3 --- C-command --- p.10 / Chapter 2.4 --- Barrier --- p.11 / Chapter 2.5 --- Government --- p.12 / Chapter 2.6 --- The Binding Theory --- p.13 / Chapter 2.7 --- Indexation --- p.16 / Chapter 2.7.1 --- Lexical Words --- p.17 / Chapter 2.7.2 --- Trace --- p.19 / Chapter 2.7.2.1 --- The ECP --- p.21 / Chapter 2.7.2.2 --- The Overt Trace --- p.23 / Chapter 2.7.3 --- PRO --- p.25 / Chapter 2.7.4 --- pro --- p.28 / Chapter 2.7.5 --- Summary --- p.29 / Chapter 2.8 --- The General Control --- p.30 / Chapter 2.9 --- Summary --- p.33 / Chapter CHAPTER 3 --- THE CLASSIFICATION OF TOPIC STRUCTURES --- p.35 / Chapter 3.1 --- The Definition --- p.35 / Chapter 3.2 --- Topicalized Topics and Left-Dislocalized Topics --- p.40 / Chapter 3.3 --- Pure Topics and Contrastive Topics --- p.42 / Chapter 3.4 --- The Topic PP --- p.47 / Chapter 3.5 --- Covert Passive Structures --- p.50 / Chapter 3.6 --- Summary --- p.54 / Chapter CHAPTER 4 --- SOME APPROACHES ON THE GENERATION OF THE TOPIC STRUCTURE --- p.56 / Chapter 4.1 --- Ross (1967) --- p.56 / Chapter 4.2 --- Chomsky (1977) --- p.59 / Chapter 4.3 --- Brunson (1992) --- p.60 / Chapter 4.4 --- "Huang (1984, 1987，1989, 1991)" --- p.61 / Chapter 4.5 --- "Xu (1985,1986, 1994)" --- p.64 / Chapter 4.6 --- Summary --- p.65 / Chapter CHAPTER 5 --- The BASE-GENERATION APPROACH --- p.67 / Chapter 5.1 --- The Identification of the Gap in the Comment --- p.67 / Chapter 5.2 --- Subjacency Effect or Control Failure? --- p.70 / Chapter 5.3 --- Free Empty Categories --- p.76 / Chapter 5.4 --- The Base-Generated Variable --- p.82 / Chapter 5.5 --- Summary --- p.84 / Chapter CHAPTER 6 --- THE TOPIC PP --- p.85 / Chapter 6.1 --- Disconstituents --- p.85 / Chapter 6.2 --- The Order Among PPs --- p.87 / Chapter 6.2.1 --- English --- p.87 / Chapter 6.2.2 --- Chinese --- p.88 / Chapter 6.2.2.1 --- Temporal Adjuncts --- p.88 / Chapter 6.2.2.2 --- Locative Adjuncts --- p.94 / Chapter 6.2.2.3 --- Relative Position of the Parallel Adjuncts --- p.96 / Chapter 6.3 --- Topic PPs and Disconstituents --- p.98 / Chapter 6.3.1 --- Topic PPs and V-bar Disconstituents --- p.98 / Chapter 6.3.2 --- Topic PPs and Subject --- p.102 / Chapter 6.3.3 --- AS for Topics and Disconstituents --- p.105 / Chapter 6.4 --- Summary --- p.107 / Chapter CHAPTER 7 --- THE RESUMPTIVE PRONOUN IN THE COMMENT --- p.109 / Chapter 7.1 --- A Controversy --- p.109 / Chapter 7.2 --- Traces and Resumptive Pronouns --- p.111 / Chapter 7.3 --- The Binding Constraint --- p.115 / Chapter 7.4 --- The Complex NP --- p.117 / Chapter 7.5 --- Why Overt? --- p.119 / Chapter 7.6 --- Summary --- p.129 / Chapter CHAPTER 8 --- CONCLUSIONS --- p.130 / BIBLIOGRAPHY --- p.135