Query: word cloud

title: An Evaluation of Semantically Grouped Word Cloud Designs
abstract: Word clouds continue to be a popular tool for summarizing textual information, despite their well documented deficiencies for analytic tasks. Much of their popularity rests on their playful visual appeal. In this paper, we present the results of a series of controlled experiments that show that layouts in which words are arranged into semantically and visually distinct zones are more effective for understanding the underlying topics than standard word cloud layouts. White space separators and/or spatially grouped color coding led to significantly stronger understanding of the underlying topics compared to a standard Wordle layout, while simultaneously scoring higher on measures of aesthetic appeal. This work is an advance on prior research on semantic layouts for word clouds because that prior work has either not ensured that the different semantic groupings are visually or semantically distinct, or has not performed usability studies. An additional contribution of this work is the development of a dataset for a semantic category identification task that can be used for replication of these results or future evaluations of word cloud designs.

title: Wordy: Interactive Word Cloud to Summarize and Browse Online Videos to Enhance eLearning
abstract: In this paper, we describe our Wordy system, which creates a word cloud based on video content and allows users to summarize and search for video content using the word cloud. We focus on lecture videos: converting speech information from the videos into text, extracting keywords from videos through morphological analysis, and creating a word cloud comprised of those keywords. In addition, the keywords in the word cloud are selectable. When a user selects a keyword, the system will find several points of the video in which a teacher talks about the keyword; the system then generates a relevant list of thumbnails. In order to save time for the user, the system offers a digest playback mode that consecutively browses the scenes concerned with the keywords. Herein, we describe our concept and the implementation of the prototype system.

title: Hindi Multi document Word Cloud based Summarization through Unsupervised Learning
abstract: Managing documents is a critical and significant task and supports many applications ranging from information retrieval to clustering search engine results. The multilinguistic facility provided by websites makes Hindi as a major language in the digital domain of information technology today. This work focuses on document management and summarization of Hindi corpus. The objective is to manage the documents and summarize Hindi corpus by applying extracting tokens and document clustering. The work is better in terms of scalability and supports consistent quality of cluster for incremental data set. Most of the past and contemporary research works have targeted English corpus document management. Hindi corpus has been mostly exploited by the researchers for exploring stemming, single document summarization and classifier design on Hindi corpus. Implementing unsupervised learning on Hindi corpus for summarization of multiple documents through Word Cloud is still an untouched area. Technically speaking, the current work is an application of TF IDF, cosine based document similarity measures and cluster dendrograms, in addition to various other Natural Language Processing (NLP) activities. Entropy and precision are used to evaluate the experiments carried on different live and available/tested datasets and results

title: News Analysis Using Word Cloud
abstract: In internet era, one can get news from huge number of sources. However, many news sources are biased in giving more coverage to specific content, persons or party. Eventually, the reader's thoughts are also influenced by the news source's biases. In this paper, a method has been proposed to instantly visualize the news topics discussed by various sources on internet. Word clouds make it very easier to decide the biases of a news source. Various algorithms, namely, Porter stemmer, Snowball, Lancaster, Rake, tf idf, text rank, and tag cloud algorithm have been tested to effectively extract the key words covered by a news source. Extraction time and count of correctly identified terms have been used as metrics to compare the algorithms. It is concluded that tf idf is better than rake and text rank algorithm due to its right balance between speed and accuracy.

title: Scaled Document Clustering and Word Cloud Based Summarization on Hindi Corpus

title: EdWordle: Consistency Preserving Word Cloud Editing
abstract: We present EdWordle, a method for consistently editing word clouds. At its heart, EdWordle allows users to move and edit words while preserving the neighborhoods of other words. To do so, we combine a constrained rigid body simulation with a neighborhood aware local Wordle algorithm to update the cloud and to create very compact layouts. The consistent and stable behavior of EdWordle enables users to create new forms of word clouds such as storytelling clouds in which the position of words is carefully edited. We compare our approach with state of the art methods and show that we can improve user performance, user satisfaction, as well as the layout itself.

title: MultiCloud: Interactive Word Cloud Visualization for the Analysis of Multiple Texts
abstract: Word Clouds have gained an impressive momentum for summarizing text documents in the last years. They visually communicate in a clear and descriptive way the most frequent words of a text. However, there are only very few word cloud visualizations that support a contrastive analysis of multiple documents. The available approaches provide comparable overviews of the documents, but have shortcomings regarding the layout, readability, and use of white space. To tackle these challenges, we propose MultiCloud, an approach to visualize multiple documents within a single word cloud in a comprehensible and visually appealing way. MultiCloud comprises several parameters and visual representations that enable users to alter the word cloud visualization in different aspects. Users can set parameters to optimize the usage of available space to get a visual representation that provides an easy visual association of words with the different documents. We evaluated MultiCloud with visualization researchers and a group of domain experts comprising five humanities scholars.

title: Beyond Word Cloud: A Graph Model Derived from Beliefs
abstract: Huge volume of text generated today poses big challenges to text mining applications. A tremendous amount of fragmented short texts are growing at a scale that makes it impossible for human to visually extract. Techniques proposed in text mining such as topic modeling dramatically improve the understanding of those unstructured and noisy texts. Naive while widely used models are word cloud and word bags. While we observed that these data models never considered the semantic relationship between the words, which make the results relatively hard to understand. One bright option is to organize those words semantically and generate an output of human understandable sentences. In this paper, we step our first foot into this direction by proposing a new data model: Belief Graph. We also proposed a schema to build belief graph model from short texts.

title: A Novel Word Cloud Graph System Design for Movie Comments
abstract: This paper presents a novel movie recommendation application system. When people want to see a movie for fun, they may just search those with high ratings, which may not be the one they are really interested in. We proposed a system to try to solve this problem. It can provide more information about the movie and help audiences find the movie they are interested in. It uses the spider and word cloud to get the comments about movies from the website and integrate them into a picture saved in a database. Audiences can get the picture from the database to know more information about the movie based on all viewers' comments. This application simplifies the movie comments into some keywords and integrates them into a picture. It provides more information about the specific movie besides ratings, to help people look for their favorite movies.

title: MultiCloud: Interactive Word Cloud Visualization for Multiple Texts
abstract: Word Clouds have gained an impressive momentum for summarizing text documents in the last years. They visually communicate in a clear and descriptive way the most frequent words of a text. However, there are only very few word cloud visualizations that support a contrastive analysis of multiple documents. The available approaches provide comparable overviews of the documents, but have shortcomings regarding the layout, readability, and use of white space. To tackle these challenges, we propose MultiCloud, an approach to visualize multiple documents within a single word cloud in a comprehensible and visually appealing way. MultiCloud comprises several parameters and visual representations that enable users to alter the word cloud visualization in different aspects. Users can set parameters to optimize the usage of available space to get a visual representation that provides an easy visual association of words with the different documents. We evaluated MultiCloud with visualization researchers and a group of domain experts comprising five humanities scholars.