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#Brexit Vs. #Stopbrexit: What is Trendier? An NLP Analysis
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml"> <head> <title>#Brexit Vs. #Stopbrexit: What is Trendier? An NLP Analysis</title> <!-- common meta tags --> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta http-equiv="X-UA-Compatible" content="ie=edge"> <meta name="title" content="#Brexit Vs. #Stopbrexit: What is Trendier? An NLP Analysis"> <meta name="description" content="Online trends have established themselves as a new method of information propagation that is reshaping journalism in the digital age. We argue that sentiment analysis鈥攖he classification of human emotion expressed in text鈥攃an enhance existing algorithms for trend discovery. By highlighting topics that are polarised, sentiment analysis can offer insight into the influence of users who are involved in a trend, and how other users adopt such a trend. As a case study, we have investigated a highly topical subject: Brexit, the withdrawal of the United Kingdom from the European Union. We retrieved an experimental corpus of publicly available tweets referring to Brexit and used them to test a proposed algorithm to identify trends. We validate the efficiency of the algorithm and gauge the sentiment expressed on the captured trends to confirm that highly polarised data ensures the emergence of trends." /> <meta name="keywords" content="Twitter; sentiment analysis; world clouds; text mining; information retrieval."/> <!-- end common meta tags --> <!-- Dublin Core(DC) meta tags --> <meta name="dc.title" content="#Brexit Vs. #Stopbrexit: What is Trendier? An NLP Analysis"> <meta name="dc.creator" content="Marco A. Palomino"> <meta name="dc.creator" content="Adithya Murali"> <meta name="dc.type" content="Article"> <meta name="dc.source" content="Computer Science & Information Technology (CS & IT) Vol.9, No.12"> <meta name="dc.date" content="2019-09-28"> <meta name="dc.identifier" content="10.5121/csit.2019.91203"> <meta name="dc.publisher" content="AIRCC Publishing Corporation"> <meta name="dc.rights" content="http://creativecommons.org/licenses/by/3.0/"> <meta name="dc.format" content="application/pdf"> <meta name="dc.language" content="en"> <meta name="dc.description" content="Online trends have established themselves as a new method of information propagation that is reshaping journalism in the digital age. We argue that sentiment analysis鈥攖he classification of human emotion expressed in text鈥攃an enhance existing algorithms for trend discovery. By highlighting topics that are polarised, sentiment analysis can offer insight into the influence of users who are involved in a trend, and how other users adopt such a trend. As a case study, we have investigated a highly topical subject: Brexit, the withdrawal of the United Kingdom from the European Union. We retrieved an experimental corpus of publicly available tweets referring to Brexit and used them to test a proposed algorithm to identify trends. We validate the efficiency of the algorithm and gauge the sentiment expressed on the captured trends to confirm that highly polarised data ensures the emergence of trends."> <meta name="dc.subject" content="Twitter"> <meta name="dc.subject" content="sentiment analysis"> <meta name="dc.subject" content="world clouds"> <meta name="dc.subject" content="text mining"> <meta name="dc.subject" content="information retrieval"> <!-- End Dublin Core(DC) meta tags --> <!-- Prism meta tags --> <meta name="prism.publicationName" content="Computer Science & Information Technology (CS & IT)"> <meta name="prism.publicationDate" content="2019-09-28"> <meta name="prism.volume" content="9"> <meta name="prism.number" content="12"> <meta name="prism.section" content="Article"> <meta name="prism.startingPage" content="19"> <!-- End Prism meta tags --> <!-- citation meta tags --> <meta name="citation_journal_title" content="Computer Science & Information Technology (CS & IT)"> <meta name="citation_publisher" content="AIRCC Publishing Corporation"> <meta name="citation_authors" content="Marco A. 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Palomino"> <meta name="citation_author" content="Adithya Murali"> <meta name="citation_doi" content="10.5121/csit.2019.91203"> <meta name="citation_abstract_html_url" content="http://aircconline.com/csit/abstract/v9n12/csit91203.html"> <meta name="citation_pdf_url" content="http://aircconline.com/csit/papers/vol9/csit91203.pdf"> <!-- end citation meta tags --> <!-- Og meta tags --> <meta property="og:site_name" content="AIRCC" /> <meta property="og:type" content="article" /> <meta property="og:url" content="http://aircconline.com/csit/abstract/v9n12/csit91203.html"/> <meta property="og:title" content="#Brexit Vs. #Stopbrexit: What is Trendier? An NLP Analysis"> <meta property="og:description" content="Online trends have established themselves as a new method of information propagation that is reshaping journalism in the digital age. We argue that sentiment analysis鈥攖he classification of human emotion expressed in text鈥攃an enhance existing algorithms for trend discovery. By highlighting topics that are polarised, sentiment analysis can offer insight into the influence of users who are involved in a trend, and how other users adopt such a trend. As a case study, we have investigated a highly topical subject: Brexit, the withdrawal of the United Kingdom from the European Union. We retrieved an experimental corpus of publicly available tweets referring to Brexit and used them to test a proposed algorithm to identify trends. We validate the efficiency of the algorithm and gauge the sentiment expressed on the captured trends to confirm that highly polarised data ensures the emergence of trends." /> <!-- end og meta tags --> <!-- INDEX meta tags --> <meta name="google-site-verification" content="t8rHIcM8EfjIqfQzQ0IdYIiA9JxDD0uUZAitBCzsOIw" /> <meta name="yandex-verification" content="e3d2d5a32c7241f4" /> <!-- end INDEX meta tags --> <link rel="icon" type="image/ico" href="../img/ico.ico"/> <link rel="stylesheet" type="text/css" href="../main1.css" media="screen" /> <style type="text/css"> a{ color:white; text-decoration:none; line-height:20px; } ul li a{ font-weight:bold; color:#000; list-style:none; text-decoration:none; size:10px;} .imagess { height:90px; text-align:left; margin:0px 5px 2px 8px; float:right; border:none; } #left p { font-family: CALIBRI; font-size: 16px; margin-left: 20px; font-weight: 500; } .right { margin-right: 20px; } #button{ float: left; font-size: 14px; margin-left: 10px; height: 28px; width: auto; background-color: #1e86c6; } </style> </head> <body> <div class="font"> <div id="wap"> <div id="page"> <div id="top"> <form action="https://airccj.org/csecfp/library/Search.php" method="get" target="_blank" > <table width="100%" cellspacing="0" cellpadding="0" > <tr class="search_input"> <td width="665" align="right"> </td> <td width="236" > <input name="title" type="text" value="Enter the paper title" class="search_textbox" onclick="if(this.value=='Enter the paper title'){this.value=''}" onblur="if(this.value==''){this.value='Enter the paper title'}" /> </td> <td width="59"> <input type="image" src="../img/go.gif" /> </td> </tr> <tr> <td colspan="3" valign="top"><img src="../img/top1.gif" alt="Academy & Industry Research Collaboration Center (AIRCC)" /></td> </tr> </table> </form> </div> <div id="font-face"> <div id="menu"> <a href="http://airccse.org">Home</a> <a href="http://airccse.org/journal.html">Journals</a> <a href="http://airccse.org/ethics.html">Ethics</a> <a href="http://airccse.org/conference.html">Conferences</a> <a href="http://airccse.org/past.html">Past Events</a> <a href="http://airccse.org/b.html">Submission</a> </div> <div id="content"> <div id="left"> <h2 class="lighter"><font size="2">Volume 9, Number 12, September 2019</font></h2> <h4 style="text-align:center;"><a>#Brexit Vs. #Stopbrexit: What is Trendier? An NLP Analysis</a></h4> <h3> Authors</h3> <p class="#left right" style="text-align:justify">Marco A. Palomino<sup>1</sup> and Adithya Murali<sup>2</sup>, <sup>1</sup>University of Plymouth, United Kingdom and <sup>2</sup>Vellore Institute of Technology, India </p> <h3> Abstract</h3> <p class="#left right" style="text-align:justify">Online trends have established themselves as a new method of information propagation that is reshaping journalism in the digital age. We argue that sentiment analysis鈥攖he classification of human emotion expressed in text鈥攃an enhance existing algorithms for trend discovery. By highlighting topics that are polarised, sentiment analysis can offer insight into the influence of users who are involved in a trend, and how other users adopt such a trend. As a case study, we have investigated a highly topical subject: Brexit, the withdrawal of the United Kingdom from the European Union. We retrieved an experimental corpus of publicly available tweets referring to Brexit and used them to test a proposed algorithm to identify trends. We validate the efficiency of the algorithm and gauge the sentiment expressed on the captured trends to confirm that highly polarised data ensures the emergence of trends. </p> <h3> Keywords</h3> <p class="#left right" style="text-align:justify">Twitter, sentiment analysis, world clouds, text mining, information retrieval </p><br> <button type="button" id="button"><a target="blank" href="/csit/papers/vol9/csit91203.pdf">Full Text</a></button> <button type="button" id="button"><a href="http://airccse.org/csit/V9N12.html">Volume 9, Number 12</a></button> <br><br><br><br><br> </div> <div id="right"> <div class="menu_right"> <ul> <li id="id"><a href="http://airccse.org/editorial.html">Editorial Board</a></li> <li><a href="http://airccse.org/arch.html">Archives</a></li> <li><a href="http://airccse.org/indexing.html">Indexing</a></li> <li><a href="http://airccse.org/faq.html" target="_blank">FAQ</a></li> </ul> </div> <h2 class="h2" align="center">Conference Proceedings</h2> <a href="http://airccse.org/cscp.html"><img src="cscf.jpg" class="img" /></a> <div class="clear_left"></div> <br> </div> <div class="clear"></div> <div id="footer"> <table width="100%" > <tr> <td width="46%" class="F_menu"><a href="http://airccse.org/subscription.html">Subscription</a> <a href="http://airccse.org/membership.html">Membership</a> <a href="http://airccse.org/cscp.html">AIRCC CSCP</a> <a href="http://airccse.org/acontact.html">Contact Us</a> </td> <td width="54%" align="right"><a href="http://airccse.org/index.php"><img src="/csit/abstract/img/logo.gif" alt="" width="21" height="24" /></a><a href="http://www.facebook.com/AIRCCSE"><img src="/csit/abstract/img/facebook.jpeg" alt="" width="21" height="24" /></a><a href="https://twitter.com/AIRCCFP"><img src="/csit/abstract/img/twitter.jpeg" alt="" width="21" height="24" /></a><a href="http://cfptech.wordpress.com/"><img src="/csit/abstract/img/index1.jpeg" alt="" width="21" height="24" /></a></td> </tr> <tr><td height="25" colspan="2"> <p align="center">All Rights Reserved ® AIRCC</p> </td></tr> </table> </div> </div> </div> </div> </div> </div> </body> </html>