Topic models are a new research field within the computer sciences information retrieval and text mining. They are generative probabilistic models of text corpora inferred by machine learning and they can be used for retrieval and text mining tasks. The most prominent topic model is latent Dirichlet allocation (LDA), which was introduced in 2003 by Blei et al. and has since then sparked off the development of other topic models for domain-specific purposes. This thesis focuses on LDA's practical application. Its main goal is the replication of the data analyses from the 2004 LDA paper ``Finding scientific topics'' by Thomas Griffiths and Mark Steyvers within the framework of the R statistical programming language and the R~package topicmodels by Bettina Grün and Kurt Hornik. The complete process, including extraction of a text corpus from the PNAS journal's website, data preprocessing, transformation into a document-term matrix, model selection, model estimation, as well as presentation of the results, is fully documented and commented. The outcome closely matches the analyses of the original paper, therefore the research by Griffiths/Steyvers can be reproduced. Furthermore, this thesis proves the suitability of the R environment for text mining with LDA. (author's abstract) / Series: Theses / Institute for Statistics and Mathematics

Submitted by Bruno Schneider (bruno.sch@gmail.com) on 2014-05-08T17:46:04Z No. of bitstreams: 1 dissertacao_bruno_schneider.pdf.pdf: 8019497 bytes, checksum: 70ff1fddb844b630666397e95c188672 (MD5) / Approved for entry into archive by Janete de Oliveira Feitosa (janete.feitosa@fgv.br) on 2014-05-13T12:56:21Z (GMT) No. of bitstreams: 1 dissertacao_bruno_schneider.pdf.pdf: 8019497 bytes, checksum: 70ff1fddb844b630666397e95c188672 (MD5) / Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2014-05-14T19:44:51Z (GMT) No. of bitstreams: 1 dissertacao_bruno_schneider.pdf.pdf: 8019497 bytes, checksum: 70ff1fddb844b630666397e95c188672 (MD5) / Made available in DSpace on 2014-05-14T19:45:33Z (GMT). No. of bitstreams: 1 dissertacao_bruno_schneider.pdf.pdf: 8019497 bytes, checksum: 70ff1fddb844b630666397e95c188672 (MD5) Previous issue date: 2014-03-21 / The combined use of algorithms for topic discovery in document collections with topic flow visualization techniques allows the exploration of thematic patterns in long corpus. In this task, those patterns could be revealed through compact visual representations. This research has investigated the requirements for viewing data about the thematic composition of documents obtained through topic modeling - where datasets are sparse and has multi-attributes - at different levels of detail through the development of an own technique and the use of an open source library for data visualization, comparatively. About the studied problem of topic flow visualization, we observed the presence of conflicting requirements for data display in different resolutions, which led to detailed investigation on ways of manipulating and displaying this data. In this study, the hypothesis put forward was that the integrated use of more than one visualization technique according to the resolution of data expands the possibilities for exploitation of the object under study in relation to what would be obtained using only one method. The exhibition of the limits on the use of these techniques according to the resolution of data exploration is the main contribution of this work, in order to provide subsidies for the development of new applications. / O uso combinado de algoritmos para a descoberta de tópicos em coleções de documentos com técnicas orientadas à visualização da evolução daqueles tópicos no tempo permite a exploração de padrões temáticos em corpora extensos a partir de representações visuais compactas. A pesquisa em apresentação investigou os requisitos de visualização do dado sobre composição temática de documentos obtido através da modelagem de tópicos – o qual é esparso e possui multiatributos – em diferentes níveis de detalhe, através do desenvolvimento de uma técnica de visualização própria e pelo uso de uma biblioteca de código aberto para visualização de dados, de forma comparativa. Sobre o problema estudado de visualização do fluxo de tópicos, observou-se a presença de requisitos de visualização conflitantes para diferentes resoluções dos dados, o que levou à investigação detalhada das formas de manipulação e exibição daqueles. Dessa investigação, a hipótese defendida foi a de que o uso integrado de mais de uma técnica de visualização de acordo com a resolução do dado amplia as possibilidades de exploração do objeto em estudo em relação ao que seria obtido através de apenas uma técnica. A exibição dos limites no uso dessas técnicas de acordo com a resolução de exploração do dado é a principal contribuição desse trabalho, no intuito de dar subsídios ao desenvolvimento de novas aplicações.

The purpose of the thesis is i) to discover the latent topics of SDG13 and their coverage in news media ii) to investigate the share of OA and Non-OA articles and reviews in each topic iii) to compare the share of different OA types (Green, Gold, Hybrid and Bronze) in each topic. It imposes a heuristic perspective and explorative approach in reviewing the three concepts open access, altmetrics and climate action (SDG13). Data is collected from SciVal, Unpaywall, Altmetric.com and Scopus rendering a dataset of 70,206 articles and reviews published between 2014-2018. The documents retrieved are analyzed with descriptive statistics and topic modeling using Sklearn’s package for LDA(Latent Dirichlet Allocation) in Python. The findings show an altmetric advantage for OA in the case of news media and SDG13 which fluctuates over topics. News media is shown to focus on subjects with “visible” effects in concordance with previous research on media coverage. Examples of this were topics concerning emissions of greenhouse gases and melting glaciers. Gold OA is the most common type being mentioned in news outlets. It also generates the highest number of news mentions while the average sum of news mentions was highest for documents published as Bronze. Moreover, the thesis is largely driven by methods used and most notably the programming language Python. As such it outlines future paths for research into the three concepts reviewed as well as methods used for topic modeling and programming.

Semantic graphs have been used for optimizing various natural language processing tasks as well as augmenting search and information retrieval tasks. In most cases these semantic graphs have been constructed through supervised machine learning methodologies that depend on manually curated ontologies such as Wikipedia or similar. In this thesis, which consists of two parts, we explore in the first part the possibility to automatically populate a semantic graph from an ad hoc data set of 50 000 newspaper articles in a completely unsupervised manner. The utility of the visual representation of the resulting graph is tested on 14 human subjects performing basic information retrieval tasks on a subset of the articles. Our study shows that, for entity finding and document similarity our feature engineering is viable and the visual map produced by our artifact is visually useful. In the second part, we explore the possibility to identify entity relationships in an unsupervised fashion by employing abstractive deep learning methods for sentence reformulation. The reformulated sentence structures are qualitatively assessed with respect to grammatical correctness and meaningfulness as perceived by 14 test subjects. We negatively evaluate the outcomes of this second part as they have not been good enough to acquire any definitive conclusion but have instead opened new doors to explore. / Semantiska grafer har använts för att optimera olika processer för naturlig språkbehandling samt för att förbättra sökoch informationsinhämtningsuppgifter. I de flesta fall har sådana semantiska grafer konstruerats genom övervakade maskininlärningsmetoder som förutsätter manuellt kurerade ontologier såsom Wikipedia eller liknande. I denna uppsats, som består av två delar, undersöker vi i första delen möjligheten att automatiskt generera en semantisk graf från ett ad hoc dataset bestående av 50 000 tidningsartiklar på ett helt oövervakat sätt. Användbarheten hos den visuella representationen av den resulterande grafen testas på 14 försökspersoner som utför grundläggande informationshämtningsuppgifter på en delmängd av artiklarna. Vår studie visar att vår funktionalitet är lönsam för att hitta och dokumentera likhet med varandra, och den visuella kartan som produceras av vår artefakt är visuellt användbar. I den andra delen utforskar vi möjligheten att identifiera entitetsrelationer på ett oövervakat sätt genom att använda abstraktiva djupa inlärningsmetoder för meningsomformulering. De omformulerade meningarna utvärderas kvalitativt med avseende på grammatisk korrekthet och meningsfullhet såsom detta uppfattas av 14 testpersoner. Vi utvärderar negativt resultaten av denna andra del, eftersom de inte har varit tillräckligt bra för att få någon definitiv slutsats, men har istället öppnat nya dörrar för att utforska.

Thesis (PhD (Industrial Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: The crucial role of innovation in creating sustainable competitive advantage is widely recognised in industry today. Likewise, the importance of having the required information accessible to the right employees at the right time is well-appreciated. More specifically, the dependency of effective, efficient innovation processes on the availability of information has been pointed out in literature. A great challenge is countering the effects of the information overload phenomenon in organisations in order for employees to find the information appropriate to their needs without having to wade through excessively large quantities of information to do so. The initial stages of the innovation process, which are characterised by free association, semi-formal activities, conceptualisation, and experimentation, have already been identified as a key focus area for improving the effectiveness of the entire innovation process. The dependency on information during these early stages of the innovation process is especially high. Any organisation requires a strategy for innovation, a number of well-defined, implemented processes and measures to be able to innovate in an effective and efficient manner and to drive its innovation endeavours. In addition, the organisation requires certain enablers to support its innovation efforts which include certain core competencies, technologies and knowledge. Most importantly for this research, enablers are required to more effectively manage and utilise innovation-related information. Information residing inside and outside the boundaries of the organisation is required to feed the innovation process. The specific sources of such information are numerous. Such information may further be structured or unstructured in nature. However, an ever-increasing ratio of available innovation-related information is of the unstructured type. Examples include the textual content of reports, books, e-mail messages and web pages. This research explores the innovation landscape and typical sources of innovation-related information. In addition, it explores the landscape of text analytical approaches and techniques in search of ways to more effectively and efficiently deal with unstructured, textual information. A framework that can be used to provide a unified, dynamic view of an organisation‟s innovation-related information, both structured and unstructured, is presented. Once implemented, this framework will constitute an innovation-focused knowledge base that will organise and make accessible such innovation-related information to the stakeholders of the innovation process. Two novel, complementary text analytical techniques, Latent Dirichlet Allocation and the Concept-Topic Model, were identified for application with the framework. The potential value of these techniques as part of the information systems that would embody the framework is illustrated. The resulting knowledge base would cause a quantum leap in the accessibility of information and may significantly improve the way innovation is done and managed in the target organisation. / AFRIKAANSE OPSOMMING: Die belangrikheid van innovasie vir die daarstel van „n volhoubare mededingende voordeel word tans wyd erken in baie sektore van die bedryf. Ook die belangrikheid van die toeganklikmaking van relevante inligting aan werknemers op die geskikte tyd, word vandag terdeë besef. Die afhanklikheid van effektiewe, doeltreffende innovasieprosesse op die beskikbaarheid van inligting word deurlopend beklemtoon in die navorsingsliteratuur. „n Groot uitdaging tans is om die oorsake en impak van die inligtingsoorvloedverskynsel in ondernemings te bestry ten einde werknemers in staat te stel om inligting te vind wat voldoen aan hul behoeftes sonder om in die proses deur oormatige groot hoeveelhede inligting te sif. Die aanvanklike stappe van die innovasieproses, gekenmerk deur vrye assosiasie, semi-formele aktiwiteite, konseptualisering en eksperimentasie, is reeds geïdentifiseer as sleutelareas vir die verbetering van die effektiwiteit van die innovasieproses in sy geheel. Die afhanklikheid van hierdie deel van die innovasieproses op inligting is besonder hoog. Om op „n doeltreffende en optimale wyse te innoveer, benodig elke onderneming „n strategie vir innovasie sowel as „n aantal goed gedefinieerde, ontplooide prosesse en metingskriteria om die innovasieaktiwiteite van die onderneming te dryf. Bykomend benodig ondernemings sekere innovasie-ondersteuningsmeganismes wat bepaalde sleutelaanlegde, -tegnologiëe en kennis insluit. Kern tot hierdie navorsing, benodig organisasies ook ondersteuningsmeganismes om hul in staat te stel om meer doeltreffend innovasie-verwante inligting te bestuur en te gebruik. Inligting, gehuisves beide binne en buite die grense van die onderneming, word benodig om die innovasieproses te voer. Die bronne van sulke inligting is veeltallig en hierdie inligting mag gestruktureerd of ongestruktureerd van aard wees. „n Toenemende persentasie van innovasieverwante inligting is egter van die ongestruktureerde tipe, byvoorbeeld die inligting vervat in die tekstuele inhoud van verslae, boeke, e-posboodskappe en webbladsye. In hierdie navorsing word die innovasielandskap asook tipiese bronne van innovasie-verwante inligting verken. Verder word die landskap van teksanalitiese benaderings en -tegnieke ondersoek ten einde maniere te vind om meer doeltreffend en optimaal met ongestruktureerde, tekstuele inligting om te gaan. „n Raamwerk wat aangewend kan word om „n verenigde, dinamiese voorstelling van „n onderneming se innovasieverwante inligting, beide gestruktureerd en ongestruktureerd, te skep word voorgestel. Na afloop van implementasie sal hierdie raamwerk die innovasieverwante inligting van die onderneming organiseer en meer toeganklik maak vir die deelnemers van die innovasieproses. Daar word verslag gelewer oor die aanwending van twee nuwerwetse, komplementêre teksanalitiese tegnieke tot aanvulling van die raamwerk. Voorts word die potensiele waarde van hierdie tegnieke as deel van die inligtingstelsels wat die raamwerk realiseer, verder uitgewys en geillustreer.

La prise en compte du cadrage fait par les influenceurs de la droite radicale et du contenu de leur discours reste peu explorée. Ces contenus sont particulièrement préoccupants lorsqu’ils sont produits par des « influenceurs » qui auraient non seulement un pouvoir social sur leurs nombreux adeptes engagés, mais qui susciteraient aussi une opposition souvent virulente envers les autorités. Certains affirment que la recherche a manqué d’études empiriques systématiques sur le sujet et l’étude de la variation de cadre serait une piste intéressante pour de futures recherches (Benford 1997). Il y a donc un besoin pressant de développer une compréhension rigoureuse de la façon dont des crises mondiales peuvent changer la façon dont certains influenceurs de la droite radicale cadrent leurs discours. En utilisant des données originales sur cinq influenceurs de la droite radicale au Québec sur la plateforme Twitter de janvier 2020 à avril 2022, nous relevons d’abord les sujets prédominants dans le discours des influenceurs de la droite radicale. Grâce à une analyse thématique par LDA, nous confirmons que sept sujets dominent le discours des influenceurs de la droite radicale durant la pandémie de COVID-19, soit les élites, la gestion de crise, les médias, la fausse pandémie, la conspiration, le gouvernement et la liberté. Deuxièmement, nous montrons que la crise sanitaire de COVID-19 a poussé les influenceurs de la droite radicale à changer leur discours et à adopter trois « cadres de crise » qui présentent la COVID-19 comme directement liée aux concepts de gouvernance, de conspiration et de liberté. / The framing done by radical right influencers and the content of their discourse remain underexplored. Such content is of serious concern when it is produced by "influencers" who would not only have social power over their many committed followers, but also would generate often virulent opposition to the authorities. Some argue that research has lacked systematic empirical studies on the topic and the study of frame variation would be an interesting avenue for future research (Benford 1997). There is thus a pressing need to develop a rigorous understanding of how global crises can change the way some radical right-wing influencers frame their discourse. Using original data about five radical right influencers in Quebec on the Twitter platform from January 2020 to April 2022, we first identify the predominant topics in radical right influencers' discourse. Through a thematic analysis by LDA, we confirm that six topics dominate the discourse of radical right influencers during the pandemic of COVID-19: elites, crisis management, media, fake pandemic, conspiracy, and freedom. Second, we show that the COVID-19 health crisis pushed radical right influencers to change their discourse and adopt three "crisis frames" that present COVID-19 as directly related to the concepts of conspiracy, governance, and freedom.

The problem of identifying clusters arising in the context of topic models and related approaches is important in the area of machine learning. The problem concerning traversals on Concept Association Networks is of great interest in the area of cognitive modelling. Cluster identification is the problem of finding the right number of clusters in a given set of points(or a dataset) in different settings including topic models and matrix factorization algorithms. Traversals in Concept Association Networks provide useful insights into cognitive modelling and performance. First, We consider the problem of authorship attribution of stylometry and the problem of cluster identification for topic models. For the problem of authorship attribution we show empirically that by using stop-words as stylistic features of an author, vectors obtained from the Latent Dirichlet Allocation (LDA) , outperforms other classifiers. Topics obtained by this method are generally abstract and it may not be possible to identify the cohesiveness of words falling in the same topic by mere manual inspection. Hence it is difficult to determine if the chosen number of topics is optimal. We next address this issue. We propose a new measure for topics arising out of LDA based on the divergence between the singular value distribution and the L1 norm distribution of the document-topic and topic-word matrices, respectively. It is shown that under certain assumptions, this measure can be used to find the right number of topics. Next we consider the Non-negative Matrix Factorization(NMF) approach for clustering documents. We propose entropy based regularization for a variant of the NMF with row-stochastic constraints on the component matrices. It is shown that when topic-splitting occurs, (i.e when an extra topic is required) an existing topic vector splits into two and the divergence term in the cost function decreases whereas the entropy term increases leading to a regularization. Next we consider the problem of clustering in Concept Association Networks(CAN). The CAN are generic graph models of relationships between abstract concepts. We propose a simple clustering algorithm which takes into account the complex network properties of CAN. The performance of the algorithm is compared with that of the graph-cut based spectral clustering algorithm. In addition, we study the properties of traversals by human participants on CAN. We obtain experimental results contrasting these traversals with those obtained from (i) random walk simulations and (ii) shortest path algorithms.

<p>Climate change has dramatically altered the ecological landscape of the eastern U.S., leading to shifts in phenological events and redistribution of tree species. However, shifts in phenology and species distributions have implications for the productivity of different populations and <a></a>the communities these species are a part of. Here, I utilized two studies to quantify the effects of climate change on forests of the eastern U.S. First, I used phenology observations at a common garden of 28 populations of northern red oak (<i>Quercus rubra</i>) across seven years to assess shifts in phenology in response to warming, identify population differences in sensitivity to warming, and correlate sensitivity to the productivity of the populations. Second, I utilized data from the USDA Forest Service’s Forest Inventory and Analysis Program to identify forest communities of the eastern U.S., assess shifts in their species compositions and spatial distributions, and determine which climate-related variables are most associated with changes at the community level. In the first study, I found that populations were shifting their spring phenology in response to warming, with the greatest sensitivity in populations from warmer, wetter climates. However, these populations with higher sensitivity did not have the highest productivity; rather, populations closer to the common garden with intermediate levels of sensitivity had the highest productivity. In the second study, I found that there were 12 regional forest communities of the eastern U.S., which varied in the amount their species composition shifted over the last three decades. Additionally, all 12 communities shifted their spatial distributions, but their shifts were not correlated with the distance and direction that climate change predicted them to shift. Finally, areas with the highest changes across all 12 communities were associated with warmer, wetter, lower temperature-variable climates generally in the southeastern U.S. Taken together, these studies provide insight into the ways in which forests are responding to climate change and have implications for the management and sustainability of forests in a continuously changing global environment.</p>

Sentiment Analysis is an area of Computer Science that deals with the impact a document makes on a user. The very field is further sub-divided into Opinion Mining and Emotion Analysis, the latter of which is the basis for the present work. Work on songs is aimed at building affective interactive applications such as music recommendation engines. Using song lyrics, we are interested in both supervised and unsupervised analyses, each of which has its own pros and cons. For an unsupervised analysis (clustering), we use a standard probabilistic topic model called Latent Dirichlet Allocation (LDA). It mines topics from songs, which are nothing but probability distributions over the vocabulary of words. Some of the topics seem sentiment-based, motivating us to continue with this approach. We evaluate our clusters using a gold dataset collected from an apt website and get positive results. This approach would be useful in the absence of a supervisor dataset. In another part of our work, we argue the inescapable existence of supervision in terms of having to manually analyse the topics returned. Further, we have also used explicit supervision in terms of a training dataset for a classifier to learn sentiment specific classes. This analysis helps reduce dimensionality and improve classification accuracy. We get excellent dimensionality reduction using Support Vector Machines (SVM) for feature selection. For re-classification, we use the Naive Bayes Classifier (NBC) and SVM, both of which perform well. We also use Non-negative Matrix Factorization (NMF) for classification, but observe that the results coincide with those of NBC, with no exceptions. This drives us towards establishing a theoretical equivalence between the two.

Text Classification (TC) which is an important field in information technology has many valuable applications. When facing the sea of information resources, the objects of TC are more complicated and diversity. The researches in pursuit of effective and practical TC technology are fairly challenging. More and more researchers regard that multi-label TC is more suited for many applications. This thesis analyses the difficulties and problems in multi-label TC and Chinese text representation based on a mass of algorithms for single-label TC and multi-label TC. Aiming at high dimensionality in feature space, sparse distribution in text representation and poor performance of multi-label classifier, this thesis will bring forward corresponding algorithms from different angles.Focusing on the problem of dimensionality “disaster” when Chinese texts are represented by using n-grams, two-step feature selection algorithm is constructed. The method combines filtering rare features within class and selecting discriminative features across classes. Moreover, the proper value of “n”, the strategy of feature weight and the correlation among features are discussed based on variety of experiments. Some useful conclusions are contributed to the research of n-gram representation in Chinese texts.In a view of the disadvantage in Latent Dirichlet Allocation (LDA) model, that is, arbitrarily revising the variable in smooth process, a new strategy for smoothing based on Tolerance Rough Set (TRS) is put forward. It constructs tolerant class in global vocabulary database firstly and then assigns value for out-of-vocabulary (oov) word in each class according to tolerant class.In order to improve performance of multi-label classifier and degrade computing complexity, a new TC method based on LDA model is applied for Chinese text representation. It extracts topics statistically from texts and then texts are represented by using the topic vector. It shows competitive performance both in English and in Chinese corpus.To enhance the performance of classifiers in multi-label TC, a compound classification framework is raised. It partitions the text space by computing the upper approximation and lower approximation. This algorithm decomposes a multi-label TC problem into several single-label TCs and several multi-label TCs which have less labels than original problem. That is, an unknown text should be classified by single-label classifier when it is partitioned into lower approximation space of some class. Otherwise, it should be classified by corresponding multi-label classifier.An application system TJ-MLWC (Tongji Multi-label Web Classifier) was designed. It could call the result from Search Engines directly and classify these results real-time using improved Naïve Bayes classifier. This makes the browse process more conveniently for users. Users could locate the texts interested immediately according to the class information given by TJ-MLWC. / La thèse est centrée sur la Classification de texte, domaine en pleine expansion, avec de nombreuses applications actuelles et potentielles. Les apports principaux de la thèse portent sur deux points : Les spécificités du codage et du traitement automatique de la langue chinoise : mots pouvant être composés de un, deux ou trois caractères ; absence de séparation typographique entre les mots ; grand nombre d’ordres possibles entre les mots d’une phrase ; tout ceci aboutissant à des problèmes difficiles d’ambiguïté. La solution du codage en «n-grams »(suite de n=1, ou 2 ou 3 caractères) est particulièrement adaptée à la langue chinoise, car elle est rapide et ne nécessite pas les étapes préalables de reconnaissance des mots à l’aide d’un dictionnaire, ni leur séparation. La classification multi-labels, c'est-à-dire quand chaque individus peut être affecté à une ou plusieurs classes. Dans le cas des textes, on cherche des classes qui correspondent à des thèmes (topics) ; un même texte pouvant être rattaché à un ou plusieurs thème. Cette approche multilabel est plus générale : un même patient peut être atteint de plusieurs pathologies ; une même entreprise peut être active dans plusieurs secteurs industriels ou de services. La thèse analyse ces problèmes et tente de leur apporter des solutions, d’abord pour les classifieurs unilabels, puis multi-labels. Parmi les difficultés, la définition des variables caractérisant les textes, leur grand nombre, le traitement des tableaux creux (beaucoup de zéros dans la matrice croisant les textes et les descripteurs), et les performances relativement mauvaises des classifieurs multi-classes habituels. / 文本分类是信息科学中一个重要而且富有实际应用价值的研究领域。随着文本分类处理内容日趋复杂化和多元化，分类目标也逐渐多样化，研究有效的、切合实际应用需求的文本分类技术成为一个很有挑战性的任务，对多标签分类的研究应运而生。本文在对大量的单标签和多标签文本分类算法进行分析和研究的基础上，针对文本表示中特征高维问题、数据稀疏问题和多标签分类中分类复杂度高而精度低的问题，从不同的角度尝试运用粗糙集理论加以解决，提出了相应的算法，主要包括：针对n-gram作为中文文本特征时带来的维数灾难问题，提出了两步特征选择的方法，即去除类内稀有特征和类间特征选择相结合的方法，并就n-gram作为特征时的n值选取、特征权重的选择和特征相关性等问题在大规模中文语料库上进行了大量的实验，得出一些有用的结论。针对文本分类中运用高维特征表示文本带来的分类效率低，开销大等问题，提出了基于LDA模型的多标签文本分类算法，利用LDA模型提取的主题作为文本特征，构建高效的分类器。在PT3多标签分类转换方法下，该分类算法在中英文数据集上都表现出很好的效果，与目前公认最好的多标签分类方法效果相当。针对LDA模型现有平滑策略的随意性和武断性的缺点，提出了基于容差粗糙集的LDA语言模型平滑策略。该平滑策略首先在全局词表上构造词的容差类，再根据容差类中词的频率为每类文档的未登录词赋予平滑值。在中英文、平衡和不平衡语料库上的大量实验都表明该平滑方法显著提高了LDA模型的分类性能，在不平衡语料库上的提高尤其明显。针对多标签分类中分类复杂度高而精度低的问题，提出了一种基于可变精度粗糙集的复合多标签文本分类框架，该框架通过可变精度粗糙集方法划分文本特征空间，进而将多标签分类问题分解为若干个两类单标签分类问题和若干个标签数减少了的多标签分类问题。即，当一篇未知文本被划分到某一类文本的下近似区域时，可以直接用简单的单标签文本分类器判断其类别；当未知文本被划分在边界域时，则采用相应区域的多标签分类器进行分类。实验表明，这种分类框架下，分类的精确度和算法效率都有较大的提高。本文还设计和实现了一个基于多标签分类的网页搜索结果可视化系统（MLWC），该系统能够直接调用搜索引擎返回的搜索结果，并采用改进的Naïve Bayes多标签分类算法实现实时的搜索结果分类，使用户可以快速地定位搜索结果中感兴趣的文本。