BiLSTM-Attention-CNN Model Based on ISSA Optimization for Cyberbullying Detection in Chinese Text

<!DOCTYPE html> <html lang="en-US" xml:lang="en-US"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> BiLSTM-Attention-CNN Model Based on ISSA Optimization for Cyberbullying Detection in Chinese Text | Information Technology and Control </title> <link rel="icon" href="https://itc.ktu.lt/public/journals/13/favicon_en_US.png"> <meta name="generator" content="Open Journal Systems 3.2.1.1"> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Wenting Fan"/> <meta name="DC.Date.created" scheme="ISO8601" content="2024-09-25"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2023-09-15"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2024-09-25"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-09-25"/> <meta name="DC.Description" xml:lang="en" content="Cyberbullying has become increasingly common due to the extent and anonymity afforded to users by online social media, and poses a significant risk to the physical and mental health of people. In this study, we propose an ISSA-based model to detect cyberbullying in Chinese text (ISSA-BiLSTM-Attention-CNN) that can determine whether a given comment reflects cyberbullying. The model contains an attention mechanism and the improved sparrow search algorithm (ISSA) for optimization that enables it to focus on important textual information and make full use of the optimal hyperparameters. Before applying the CNN to collect and learn a sufficient number of local features, the model initially uses the bidirectional LSTM (BiLSTM) to concatenate the results of forward and backward processing of the given text. The results of experiments showed that the proposed method can outperform baseline methods, with an accuracy of 90.2% and an f-measure of 89.9%."/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="35112"/> <meta name="DC.Identifier.pageNumber" content="659-674"/> <meta name="DC.Identifier.DOI" content="10.5755/j01.itc.53.3.35112"/> <meta name="DC.Identifier.URI" content="https://itc.ktu.lt/index.php/ITC/article/view/35112"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2024 Information Technology and Control "/> <meta name="DC.Rights" content=""/> <meta name="DC.Source" content="Information Technology and Control "/> <meta name="DC.Source.ISSN" content="2335-884X"/> <meta name="DC.Source.Issue" content="3"/> <meta name="DC.Source.Volume" content="53"/> <meta name="DC.Source.URI" content="https://itc.ktu.lt/index.php/ITC"/> <meta name="DC.Subject" xml:lang="en" content="CNN"/> <meta name="DC.Title" content="BiLSTM-Attention-CNN Model Based on ISSA Optimization for Cyberbullying Detection in Chinese Text"/> <meta name="DC.Type" content="Text.Serial.Journal"/> <meta name="DC.Type.articleType" content="Articles"/> <meta name="gs_meta_revision" content="1.1"/> <meta name="citation_journal_title" content="Information Technology and Control "/> <meta name="citation_journal_abbrev" content="ITC"/> <meta name="citation_issn" content="2335-884X"/> <meta name="citation_author" content="Wenting Fan"/> <meta name="citation_author_institution" content="+8617745842569"/> <meta name="citation_title" content="BiLSTM-Attention-CNN Model Based on ISSA Optimization for Cyberbullying Detection in Chinese Text"/> <meta name="citation_language" content="en"/> <meta name="citation_date" content="2024/09/25"/> <meta name="citation_volume" content="53"/> <meta name="citation_issue" content="3"/> <meta name="citation_firstpage" content="659"/> <meta name="citation_lastpage" 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class="pkp_structure_content has_sidebar"> <div class="pkp_structure_main" role="main"> <a id="pkp_content_main"></a> <div class="page page_article"> <nav class="cmp_breadcrumbs" role="navigation" aria-label="You are here:"> <ol> <li> <a href="https://itc.ktu.lt/index.php/ITC/index"> Home </a> <span class="separator">></span> </li> <li> <a href="https://itc.ktu.lt/index.php/ITC/issue/archive"> Archives </a> <span class="separator">></span> </li> <li> <a href="https://itc.ktu.lt/index.php/ITC/issue/view/990"> Vol. 53 No. 3 (2024) </a> <span class="separator">></span> </li> <li class="current" aria-current="page"> <span aria-current="page"> Articles </span> </li> </ol> </nav> <article class="obj_article_details"> <h1 class="page_title"> BiLSTM-Attention-CNN Model Based on ISSA Optimization for Cyberbullying Detection in Chinese Text </h1> <div class="row"> <div class="main_entry"> <section class="item authors"> <h2 class="pkp_screen_reader">Authors</h2> <ul class="authors"> <li> <span class="name"> Wenting Fan </span> <span class="affiliation"> +8617745842569 </span> </li> </ul> </section> <section class="item doi"> <h2 class="label"> DOI: </h2> <span class="value"> <a href="https://doi.org/10.5755/j01.itc.53.3.35112"> https://doi.org/10.5755/j01.itc.53.3.35112 </a> </span> </section> <section class="item keywords"> <h2 class="label"> Keywords: </h2> <span class="value"> cyberbullying detection, attention mechanism, improved sparrow search algorithm (ISSA), bidirectional LSTM (BiLSTM), CNN </span> </section> <section class="item abstract"> <h2 class="label">Abstract</h2> <p>Cyberbullying has become increasingly common due to the extent and anonymity afforded to users by online social media, and poses a significant risk to the physical and mental health of people. In this study, we propose an ISSA-based model to detect cyberbullying in Chinese text (ISSA-BiLSTM-Attention-CNN) that can determine whether a given comment reflects cyberbullying. The model contains an attention mechanism and the improved sparrow search algorithm (ISSA) for optimization that enables it to focus on important textual information and make full use of the optimal hyperparameters. Before applying the CNN to collect and learn a sufficient number of local features, the model initially uses the bidirectional LSTM (BiLSTM) to concatenate the results of forward and backward processing of the given text. The results of experiments showed that the proposed method can outperform baseline methods, with an accuracy of 90.2% and an f-measure of 89.9%.</p> </section> </div> <div class="entry_details"> <div class="item galleys"> <h2 class="pkp_screen_reader"> Downloads </h2> <ul class="value galleys_links"> <li> <a class="obj_galley_link pdf" href="https://itc.ktu.lt/index.php/ITC/article/view/35112/16576"> PDF </a> </li> </ul> </div> <div class="item published"> <section class="sub_item"> <h2 class="label"> Published </h2> <div class="value"> <span>2024-09-25</span> </div> </section> </div> <div class="item issue"> <section class="sub_item"> <h2 class="label"> Issue </h2> <div class="value"> <a class="title" href="https://itc.ktu.lt/index.php/ITC/issue/view/990"> Vol. 53 No. 3 (2024) </a> </div> </section> <section class="sub_item"> <h2 class="label"> Section </h2> <div class="value"> Articles </div> </section> </div> <div class="item copyright"> <h2 class="label"> License </h2> <p>Copyright terms are indicated in the Republic of Lithuania Law on Copyright and Related Rights, Articles 4-37.</p> </div> </div> </div> </article> </div> </div> <div class="pkp_structure_sidebar left" role="complementary" aria-label="Sidebar"> <div class="pkp_block block_custom" id="customblock-Crossref"> <div class="content"> <p> </p> <p><img src="https://itc.ktu.lt/public/site/images/testadmin/scopus.jpg" alt="" width="254" height="525"></p> <p> </p> </div> </div> <div class="pkp_block block_custom" id="customblock-CrossRef2"> <div class="content"> <p><a style="background: none;" href="http://www.crossref.org/" target="_blank" rel="noopener"><img src="/styles/images/crossref.gif" alt="crossref" width="90" height="32" border="0"></a>    <a style="background: none;" href="http://www.crossref.org/crosscheck/index.html" target="_blank" rel="noopener"><img src="/styles/images/crosscheck.gif" alt="crosscheck" width="90" height="32" border="0"></a></p> <p><a style="background: none;" href="http://research.ithenticate.com" target="_blank" rel="noopener"> <img src="/styles/images/ithenticate_1.png" alt="iThenticate" width="180" height="87" border="0"></a></p> <p><img src="https://itc.ktu.lt/public/site/images/testadmin/mceclip1.png" width="178" height="105"></p> </div> </div> <div class="pkp_block block_information"> <h2 class="title">Information</h2> <div class="content"> <ul> <li> <a href="https://itc.ktu.lt/index.php/ITC/information/readers"> For Readers </a> </li> <li> <a href="https://itc.ktu.lt/index.php/ITC/information/authors"> For Authors </a> </li> <li> <a href="https://itc.ktu.lt/index.php/ITC/information/librarians"> For Librarians </a> </li> </ul> </div> </div> </div> </div> <div class="pkp_structure_footer_wrapper" role="contentinfo"> <a id="pkp_content_footer"></a> <div class="pkp_structure_footer"> <div class="pkp_footer_content"> <p>Print ISSN: 1392-124X <br>Online ISSN: 2335-884X</p> </div> <div class="pkp_brand_footer" role="complementary"> <a href="https://itc.ktu.lt/index.php/ITC/about/aboutThisPublishingSystem"> <img alt="More information about the publishing system, Platform and Workflow by OJS/PKP." src="https://itc.ktu.lt/templates/images/ojs_brand.png"> </a> </div> </div> </div> </div> <script src="https://itc.ktu.lt/lib/pkp/lib/vendor/components/jquery/jquery.min.js?v=3.2.1.1" type="text/javascript"></script><script src="https://itc.ktu.lt/lib/pkp/lib/vendor/components/jqueryui/jquery-ui.min.js?v=3.2.1.1" type="text/javascript"></script><script src="https://itc.ktu.lt/lib/pkp/js/lib/jquery/plugins/jquery.tag-it.js?v=3.2.1.1" type="text/javascript"></script><script src="https://itc.ktu.lt/plugins/themes/default/js/lib/popper/popper.js?v=3.2.1.1" type="text/javascript"></script><script src="https://itc.ktu.lt/plugins/themes/default/js/lib/bootstrap/util.js?v=3.2.1.1" type="text/javascript"></script><script src="https://itc.ktu.lt/plugins/themes/default/js/lib/bootstrap/dropdown.js?v=3.2.1.1" type="text/javascript"></script><script src="https://itc.ktu.lt/plugins/themes/default/js/main.js?v=3.2.1.1" type="text/javascript"></script><script type="text/javascript"> (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o), m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m) })(window,document,'script','https://www.google-analytics.com/analytics.js','ga'); ga('create', 'UA-27657212-1', 'auto'); ga('send', 'pageview'); </script> </body> </html>

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BiLSTM-Attention-CNN Model Based on ISSA Optimization for Cyberbullying Detection in Chinese Text | Information Technology and Control