User generated reviews, like those found on Yelp and Amazon, have become important reference material in casual decision making, like dining, shopping and entertainment. However, very large amounts of reviews make the review reading process time consuming. A text visualization can speed up the review reading process. In this thesis, we present the clustered layout word cloud -- a text visualization that quickens decision making based on user generated reviews. We used a natural language processing approach, called grammatical dependency parsing, to analyze user generated review content and create a semantic graph. A force-directed graph layout was applied to the graph to create the clustered layout word cloud. We conducted a two-task user study to compare the clustered layout word cloud to two alternative review reading techniques: random layout word cloud and normal block-text reviews. The results showed that the clustered layout word cloud offers faster task completion time and better user satisfaction than the other two alternative review reading techniques. [Permission email from J. Huang removed at his request. GMc March 11, 2014] / Master of Science

We present a novel approach to text visualization called descriptive non-photorealistic rendering which exploits the inherent spatial and abstract dimensions in text documents to integrate 3D non-photorealistic rendering with information visualization. The visualization encodes text data onto 3D models, emphasizing the relative signi ficance of words in the text and the physical, real-world relationships between those words. Analytic exploration is supported through a collection of interactive widgets and direct multitouch interaction with the 3D models. We applied our method to analyze a collection of vehicle complaint reports from National Highway Traffic Safety Administration (NHTSA), and through a qualitative evaluation study, we demonstrate how our system can support tasks such as comparing the reliability of di fferent makes and models, finding interesting facts, and revealing possible causal relations between car parts. / UOIT

abstract: Since the advent of the internet and even more after social media platforms, the explosive growth of textual data and its availability has made analysis a tedious task. Information extraction systems are available but are generally too specific and often only extract certain kinds of information they deem necessary and extraction worthy. Using data visualization theory and fast, interactive querying methods, leaving out information might not really be necessary. This thesis explores textual data visualization techniques, intuitive querying, and a novel approach to all-purpose textual information extraction to encode large text corpus to improve human understanding of the information present in textual data. This thesis presents a modified traversal algorithm on dependency parse output of text to extract all subject predicate object pairs from text while ensuring that no information is missed out. To support full scale, all-purpose information extraction from large text corpuses, a data preprocessing pipeline is recommended to be used before the extraction is run. The output format is designed specifically to fit on a node-edge-node model and form the building blocks of a network which makes understanding of the text and querying of information from corpus quick and intuitive. It attempts to reduce reading time and enhancing understanding of the text using interactive graph and timeline. / Dissertation/Thesis / Masters Thesis Software Engineering 2018

No description available.

Text visualization is a field dedicated to the visual representation of textual data by using computer technology. A large number of visualization techniques are available, and now it is becoming harder for researchers and practitioners to choose an optimal technique for a particular task among the existing techniques. To overcome this problem, the ISOVIS Group developed an interactive survey browser for text visualization techniques. ISOVIS researchers gathered papers which describe text visualization techniques or tools and categorized them according to a taxonomy. Several categories were manually assigned to each visualization technique. In this thesis, we aim to analyze the dataset of this browser. We carried out several analyses to find temporal trends and correlations of the categories present in the browser dataset. In addition, a comparison of these categories with a computational approach has been made. Our results show that some categories became more popular than before whereas others have declined in popularity. The cases of positive and negative correlation between various categories have been found and analyzed. Comparison between manually labeled datasets and results of computational text analyses were presented to the experts with an opportunity to refine the dataset. Data which is analyzed in this thesis project is specific to text visualization field, however, methods that are used in the analyses can be generalized for applications to other datasets of scientific literature surveys or, more generally, other manually curated collections of textual documents.

Ma dissertation explore comment l'intégration de petites visualisations contextuelles basées sur des données peut complémenter des documents écrits. Plus spécifiquement, j'identifie et je définis des aspects importants et des directions de recherches pertinentes pour l'intégration de petites visualisations contextuelles basées sur des données textuelles. Cette intégration devra finalement devenir aussi fluide qu'écrire et aussi utile que lire un texte. Je définis les visualisations-mots (Word-Scale Visualizations) comme étant de petites visualisations contextuelles basées sur des données intégrées au texte de documents. Ces visualisations peuvent utiliser de multiples codages visuels incluant les cartes géographiques, les heatmaps, les graphes circulaires, et des visualisations plus complexes. Les visualisations-mots offrent une grande variété de dimensions toujours proches de l’échelle d’un mot, parfois plus grandes, mais toujours plus petites qu’une phrase ou un paragraphe. Les visualisations-mots peuvent venir en aide et être utilisées dans plusieurs formes de discours écrits tels les manuels, les notes, les billets de blogs, les rapports, les histoires, ou même les poèmes. En tant que complément visuel de textes, les visualisations-mots peuvent être utilisées pour accentuer certains éléments d'un document (comme un mot ou une phrase), ou pour apporter de l'information additionnelle. Par exemple, un petit diagramme de l'évolution du cours de l’action d’une entreprise peut être intégré à côté du nom de celle-ci pour apporter de l'information additionnelle sur la tendance passée du cours de l'action. Dans un autre exemple, des statistiques de jeux peuvent être incluses à côté du nom d'équipe de football ou de joueur dans les articles concernant le championnat d'Europe de football. Ces visualisations-mots peuvent notamment aider le lecteur à faire des comparaisons entre le nombre de passes des équipes et des joueurs. Le bénéfice majeur des visualisations-mots réside dans le fait que le lecteur peut rester concentré sur le texte, vu que les visualisations sont dans le texte et non à côté.Dans ma thèse j’apporte les contributions suivantes : j'explore pourquoi les visualisation-mots peuvent être utiles et comment promouvoir leur création. J’étudie différentes options de placement pour les visualisations-mots et je quantifie leurs effets sur la disposition du texte et sa mise en forme. Comme les visualisations-mots ont aussi des implications sur le comportement de lecture du lecteur, je propose une première étude qui étudie les différents placements de visualisations-mots sur le comportement de lecture. J'examine aussi comment combiner les visualisations-mots et l'interaction pour soutenir une lecture plus active en proposant des méthodes de collection, d’arrangement et de comparaison de visualisations-mots. Finalement, je propose des considérations de design pour la conception et la création de visualisations-mots et je conclus avec des exemples d'application.En résumé cette dissertation contribue à la compréhension de petites visualisations contextuelles basées sur des données intégrées dans le texte et à leur utilité pour la visualisation d'informations. / This dissertation explores how embedding small data-driven contextual visualizations can complement text documents. More specifically, I identify and define important aspects and relevant research directions for the integration of small data-driven contextual visualizations into text. This integration should eventually become as fluid as writing and as usable as reading a text. I define word-scale visualisations as small data-driven contextual visualizations embedded in text documents. These visualizations can use various visual encodings including geographical maps, heat maps, pie charts, and more complex visualizations. They can appear at a range of word scales, including sizes larger than a letter, but smaller than a sentence or paragraph. Word-scale visualisations can help support and be used in many forms of written discourse such as text books, notes, blog posts, reports, stories, or poems. As graphical supplements to text, word-scale visualisations can be used to emphasize certain elements of a document (e.g. a word or a sentence), or to provide additional information. For example, a small stock chart can be embedded next to the name of a company to provide additional information about the past trends of its stocks. In another example, game statistics can be embedded next to the names of soccer teams or players in daily reports from the UEFA European Championship. These word-scale visualisations can then for example allow readers to make comparison between number of passes of teams and players. The main benefit of word-scale visualisations is that the reader can remain focused on the text as the visualization are within the text rather than alongside it.In the thesis, I make the following main contributions: I explore why word-scale visualisations can be useful and how to support their creation. I investigate placement options to embed word-scale visualisations and quantify their effects on the layout and flow of the text. As word-scale visualisations also have implications on the reader's reading behavior I propose a first study that investigates different word-scale visualisation positions on the reading behavior. I also explore how word-scale visualisations can be combined with interaction to support a more active reading by proposing interaction methods to collect, arrange and compare word-scale visualisations. Finally, I propose design considerations for the authoring of word-scale visualisations and conclude with application examples.In summary, this dissertation contributes to the understanding of small data-driven contextual visualizations embedded into text and their value for Information Visualization.

A análise de dados multidimensionais tem sido por muitos anos tópico de contínua investigação e uma das razões se deve ao fato desse tipo de dados ser encontrado em diversas áreas da ciência. Uma tarefa comum ao se analisar esse tipo de dados é a investigação de padrões pela interação em projeções multidimensionais dos dados para o espaço visual. O entendimento da relação entre as características do conjunto de dados (dataset) e a técnica utilizada para se obter uma representação visual desse dataset é de fundamental importância uma vez que esse entendimento pode fornecer uma melhor intuição a respeito do que se esperar da projeção. Por isso motivado, no presente trabalho investiga-se alguns aspectos de centralidade dos dados em dois cenários distintos: coleções de documentos com grafos de coautoria; dados multidimensionais mais gerais. No primeiro cenário, o dado multidimensional que representa os documentos possui informações mais específicas, o que possibilita a combinação de diferentes aspectos para analisá-los de forma sumarizada, bem como a noção de centralidade e relevância dentro da coleção. Isso é levado em consideração para propor uma metáfora visual combinada que possibilite a exploração de toda a coleção, bem como de documentos individuais. No segundo cenário, de dados multidimensionais gerais, assume-se que tais informações não estão disponíveis. Ainda assim, utilizando um conceito de estatística não-paramétrica, deno- minado funções de profundidade de dados (data-depth functions), é feita a avaliação da ação de técnicas de projeção multidimensionais sobre os dados, possibilitando entender como suas medidas de profundidade (centralidade) foram alteradas ao longo do processo, definindo uma também medida de qualidade para projeções. / Analysis of multidimensional data has been for many years a topic of continuous research and one of the reasons is such kind of data can be found on several different areas of science. A common task analyzing such data is to investigate patterns by interacting with spatializations of the data onto the visual space. Understanding the relation between underlying dataset characteristics and the technique used to provide a visual representation of such dataset is of fundamental importance since it can provide a better intuition on what to expect from the spatialization. Motivated by this, in this work we investigate some aspects of centrality on the data in two different scenarios: document collection with co-authorship graphs; general multidimensional data. In the first scenario, the multidimensional data which encodes the documents is much more information specific, meaning it makes possible to combine different aspects such as a summarized analysis, as well as the centrality and relevance notions among the documents in the collection. In order to propose a combined visual metaphor, this is taken into account make possible the visual exploration of the whole document collection as well as individual document analysis. In the second case, of general multidimensional data, there is an assumption that such additional information is not available. Nevertheless, using the concept of data-depth functions from non-parametric statistics it is analyzed the action of multidimensional projection techniques on the data, during the projection process, in order to make possible to understand how depth measures computed in the data have been modified along the process, which also defines a quality measure for multidimensional projections.

A análise de dados multidimensionais tem sido por muitos anos tópico de contínua investigação e uma das razões se deve ao fato desse tipo de dados ser encontrado em diversas áreas da ciência. Uma tarefa comum ao se analisar esse tipo de dados é a investigação de padrões pela interação em projeções multidimensionais dos dados para o espaço visual. O entendimento da relação entre as características do conjunto de dados (dataset) e a técnica utilizada para se obter uma representação visual desse dataset é de fundamental importância uma vez que esse entendimento pode fornecer uma melhor intuição a respeito do que se esperar da projeção. Por isso motivado, no presente trabalho investiga-se alguns aspectos de centralidade dos dados em dois cenários distintos: coleções de documentos com grafos de coautoria; dados multidimensionais mais gerais. No primeiro cenário, o dado multidimensional que representa os documentos possui informações mais específicas, o que possibilita a combinação de diferentes aspectos para analisá-los de forma sumarizada, bem como a noção de centralidade e relevância dentro da coleção. Isso é levado em consideração para propor uma metáfora visual combinada que possibilite a exploração de toda a coleção, bem como de documentos individuais. No segundo cenário, de dados multidimensionais gerais, assume-se que tais informações não estão disponíveis. Ainda assim, utilizando um conceito de estatística não-paramétrica, deno- minado funções de profundidade de dados (data-depth functions), é feita a avaliação da ação de técnicas de projeção multidimensionais sobre os dados, possibilitando entender como suas medidas de profundidade (centralidade) foram alteradas ao longo do processo, definindo uma também medida de qualidade para projeções. / Analysis of multidimensional data has been for many years a topic of continuous research and one of the reasons is such kind of data can be found on several different areas of science. A common task analyzing such data is to investigate patterns by interacting with spatializations of the data onto the visual space. Understanding the relation between underlying dataset characteristics and the technique used to provide a visual representation of such dataset is of fundamental importance since it can provide a better intuition on what to expect from the spatialization. Motivated by this, in this work we investigate some aspects of centrality on the data in two different scenarios: document collection with co-authorship graphs; general multidimensional data. In the first scenario, the multidimensional data which encodes the documents is much more information specific, meaning it makes possible to combine different aspects such as a summarized analysis, as well as the centrality and relevance notions among the documents in the collection. In order to propose a combined visual metaphor, this is taken into account make possible the visual exploration of the whole document collection as well as individual document analysis. In the second case, of general multidimensional data, there is an assumption that such additional information is not available. Nevertheless, using the concept of data-depth functions from non-parametric statistics it is analyzed the action of multidimensional projection techniques on the data, during the projection process, in order to make possible to understand how depth measures computed in the data have been modified along the process, which also defines a quality measure for multidimensional projections.