Word Cloud Generation Research Articles

Scientific research publications in dengue: A global and Indian bibliometric analysis from 1997 to 2018

Dengue fever is common in tropical and subtropical countries of the world and its present impact is 30 times more than what it was 50 yr ago. The aim of the study was to analyze the research output/publications on dengue across the world as well as in India, using the Web of Science database. Literature search was done using the keyword 'dengue' in the title field to find global and Indian publications on dengue from 1 June 1997 to 20 July 2018. Bibliometric analysis was done on the basis of the magnitude of research output; year, type and language of publications; citations and h-index, impact factor, keywords, most productive institutes etc. Descriptive analysis was performed using MS Office, Word Cloud Generator and SPSS software. The analysis of the records obtained indicated that globally 11,742 articles were published on dengue during the period of 21 yr of which 1114 were Indian. The country with highest number of publications was United States (3449; 29.37%) while India ranked third (1114; 9.48%). Majority of the published articles where in the American Journal of Tropical Medicine and Hygiene (1366; 11.63%) and the leading journal in India was Indian Journal of Medical Research (56; 5.02%) followed by the Journal of Vector Borne Diseases (42; 3.77%). The most productive Institute at global level was 'Mahidol University, Thailand' while in the Indian context it was 'All India Institute of Medical Sciences, New Delhi'. The elevated metrics of citation, impact factor, and h-index are based on the magnitude of scientific research outputs. Increasing trend in the number of publications over the years both globally as well as in India indicates overall growth in dengue research. Even though it is a good sign of productivity it is still significantly related to the magnitude of occurrence of dengue cases across the globe. Further, it is a good indication that the scientific community is dynamic and responding well in bringing out the regional scenarios to the public for health promotion.

Assessment of Orthopedic Educational Research in 2015 Publications

Due to orthopedic surgery's unique educational model, recent changes within graduate medical education have the potential to impact the methods and outcomes of specialty training significantly. Using Mind the Gap: Representation of Medical Education in Cardiology-Related Articles and Journals (Allred et al., 2016) as a framework, this study investigates educational research within the field of orthopedics. Two main objectives were evaluated: (1) quantitative analysis of the amount and type of orthopedic education-related research in orthopedic, general surgery, and medical education journals and (2) estimate the priority of orthopedic journals to publish education-focused articles. Using a composite citation-based 5-metric scoring system, a complete list of journals pertaining to orthopedics, nonorthopedic specific surgery journals, and general medical education journals was ranked. All publications during 2015 for the selected cohort of journals were then analyzed for orthopedic education-related publications. Aim and scope of the top 15 selected orthopedic journals (along with the mission statements of their associated societies) were evaluated with a word cloud generator to determine priority on education. Review of 7112 articles from the top 15 selected orthopedic journals yielded 37 publications with an educational focus. Evaluation of 15 general surgery or medical education journals, containing 4661 publications, generated an additional 28 positive articles. In total, 51 unique orthopedic education-related publications (0.43% of total evaluated articles) were identified from the 11,773 articles published in the selected 30 journals for 2015. The lack of emphasis on orthopedic educational research output is multifactorial, needing further evaluation to determine specific causes and methods of improvement. This article adequately sheds light on the need to increase support of educational research programs within the field of orthopedics.

Development of Word Cloud Generator Software Based on Python

A word cloud is a kind of weighted list to visualize language or text data, which gains increasing attention and more application opportunities as the big data time approaches. Currently, there has been some online word cloud generators available for users with simple requests, such as repeating the exact phrase, or collecting the text data from a web page. Moreover, most current word cloud generators cannot support characters other than English, which are limited in English-speaking users. There are also packages for programming languages (such as Python and R) to generate word clouds, which requires coding and is not user-friendly. This paper is focusing on developing a graphical user interface (GUI) software to generate word cloud maps with easy operations. The Python programming language is involved and some details are discussed. Finally the software is released for further application.

Morphable Word Clouds for Time-Varying Text Data Visualization.

A word cloud is a visual representation of a collection of text documents that uses various font sizes, colors, and spaces to arrange and depict significant words. The majority of previous studies on time-varying word clouds focuses on layout optimization and temporal trend visualization. However, they do not fully consider the spatial shapes and temporal motions of word clouds, which are important factors for attracting people's attention and are also important cues for human visual systems in capturing information from time-varying text data. This paper presents a novel method that uses rigid body dynamics to arrange multi-temporal word-tags in a specific shape sequence under various constraints. Each word-tag is regarded as a rigid body in dynamics. With the aid of geometric, aesthetic, and temporal coherence constraints, the proposed method can generate a temporally morphable word cloud that not only arranges word-tags in their corresponding shapes but also smoothly transforms the shapes of word clouds over time, thus yielding a pleasing time-varying visualization. Using the proposed frame-by-frame and morphable word clouds, people can observe the overall story of a time-varying text data from the shape transition, and people can also observe the details from the word clouds in frames. Experimental results on various data demonstrate the feasibility and flexibility of the proposed method in morphable word cloud generation. In addition, an application that uses the proposed word clouds in a simulated exhibition demonstrates the usefulness of the proposed method.

Personalized generation of word clouds from tweets

Active users of Twitter are often overwhelmed with the vast amount of tweets. In this work we attempt to help users browsing a large number of accumulated posts. We propose a personalized word cloud generation as a means for users' navigation. Various user past activities such as user published tweets, retweets, and seen but not retweeted tweets are leveraged for enhanced personalization of word clouds. The best personalization results are attained with user past retweets. However, users' own past tweets are not as useful as retweets for personalization. Negative preferences derived from seen but not retweeted tweets further enhance personalized word cloud generation. The ranking combination method outperforms the preranking approach and provides a general framework for combined ranking of various user past information for enhanced word cloud generation. To better capture subtle differences of generated word clouds, we propose an evaluation of word clouds with a mean average precision measure.

Folksonomy-Based Term Extraction for Word Cloud Generation

In this work we study the task of term extraction for word cloud generation in sparsely tagged domains, in which manual tags are scarce. We present a folksonomy-based term extraction method, called tag-boost , which boosts terms that are frequently used by the public to tag content. Our experiments with tag-boost based term extraction over different domains demonstrate tremendous improvement in word cloud quality, as reflected by the agreement between manual tags of the testing items and the cloud’s terms extracted from the items’ content. Moreover, our results demonstrate the high robustness of this approach, as compared to alternative cloud generation methods that exhibit a high sensitivity to data sparseness. Additionally, we show that tag-boost can be effectively applied even in nontagged domains, by using an external rich folksonomy borrowed from a well-tagged domain.

Genes2WordCloud: a quick way to identify biological themes from gene lists and free text

BackgroundWord-clouds recently emerged on the web as a solution for quickly summarizing text by maximizing the display of most relevant terms about a specific topic in the minimum amount of space. As biologists are faced with the daunting amount of new research data commonly presented in textual formats, word-clouds can be used to summarize and represent biological and/or biomedical content for various applications.ResultsGenes2WordCloud is a web application that enables users to quickly identify biological themes from gene lists and research relevant text by constructing and displaying word-clouds. It provides users with several different options and ideas for the sources that can be used to generate a word-cloud. Different options for rendering and coloring the word-clouds give users the flexibility to quickly generate customized word-clouds of their choice.MethodsGenes2WordCloud is a word-cloud generator and a word-cloud viewer that is based on WordCram implemented using Java, Processing, AJAX, mySQL, and PHP. Text is fetched from several sources and then processed to extract the most relevant terms with their computed weights based on word frequencies. Genes2WordCloud is freely available for use online; it is open source software and is available for installation on any web-site along with supporting documentation at http://www.maayanlab.net/G2W.ConclusionsGenes2WordCloud provides a useful way to summarize and visualize large amounts of textual biological data or to find biological themes from several different sources. The open source availability of the software enables users to implement customized word-clouds on their own web-sites and desktop applications.