Topical Collection "Computer Science in Sport"

A topical collection in Applied Sciences (ISSN 2076-3417). This collection belongs to the section "Computing and Artificial Intelligence".

Editor

Dr. Christian W. Dawson
Website
Collection Editor

Topical Collection Information

Dear Colleagues,

Computer Science in Sport is a cross-disciplinary topic that brings together the problem-solving capabilities of Computer Science to various theoretical and practical aspects of all sports and physical activities. Applications cover a diverse range, including the analysis of individuals and teams in competition and training; equipment design and assessment (which can include playing surfaces and clothing); biomechanics; physiological analysis; injury prediction and prevention; and tactical analysis and modelling. Areas of Computer Science that have been utilized include image processing, data mining, artificial intelligence, virtual reality, wearable devices, ubiquitous computing, and sensor technologies, to name a few.

This Topical Collection aims to bring together the latest research and ideas in this cross-disciplinary area. Its focus is on the capturing of individual and team performance during training and competition and using these data to enhance performance in the future.

Dr. Christian W. Dawson
Collection Editor 

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computer science
  • team sports
  • individual performance analysis
  • biomechanics
  • physiology

Published Papers (1 paper)

2020

Open AccessArticle
Innovative Approaches in Sports Science—Lexicon-Based Sentiment Analysis as a Tool to Analyze Sports-Related Twitter Communication
Appl. Sci. 2020, 10(2), 431; https://doi.org/10.3390/app10020431 - 07 Jan 2020
Abstract
Sentiment analysis refers to the algorithmic extraction of subjective information from textual data and—driven by the increasing amount of online communication—has become one of the fastest growing research areas in computer science with applications in several domains. Although sports events such as football [...] Read more.
Sentiment analysis refers to the algorithmic extraction of subjective information from textual data and—driven by the increasing amount of online communication—has become one of the fastest growing research areas in computer science with applications in several domains. Although sports events such as football matches are accompanied by a huge public interest and large amount of related online communication, social media analysis in general and sentiment analysis in particular are almost unused tools in sports science so far. The present study tests the feasibility of lexicon-based tools of sentiment analysis with regard to football-related textual data on the microblogging platform Twitter. The sentiment of a total of 10,000 tweets with reference to ten top-level football matches was analyzed both manually by human annotators and algorithmically by means of publicly available sentiment analysis tools. Results show that the general sentiment of realistic sets (1000 tweets with a proportion of 60% having the same polarity) can be classified correctly with more than 95% accuracy. The present paper demonstrates that sentiment analysis can be an effective and useful tool for sports-related content and is intended to stimulate the increased use of and discussion on sentiment analysis in sports science. Full article
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