[DBWorld] *New Deadline* CfP: Special Issue on Deep Learning and Explainability for Sentiment Analysis
Danilo Dessi via DBWorld <[email protected]> Tue, 15 Jun 2021 01:13:48 -0500 (CDT)
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<p><span style="font-size: 13px;">CfP: Special Issue on Deep Learning and Explainability for Sentiment Analysis - Electronics (IF: 2.412)<span style="color: rgb(252, 44, 0);"><br></span></span></p>
<p><span style="font-size: 13px;">Webpage: <a href="https://www.mdpi.com/journal/electronics/special_issues/SA_electronics">https://www.mdpi.com/journal/electronics/special_issues/SA_electronics</a></span></p>
<p><span style="font-size: 13px;">Deadline: <strong><span style="color: rgb(252, 44, 0);">Dec 31, 2021</span></strong></span></p>
<p><span style="font-size: 13px;"><strong><br>------------------------------------------------------<br><br>Summary<br><br>------------------------------------------------------</strong></span></p>
<p><span style="font-size: 13px;">People use online social platforms to express opinions about products and/or services in a wide range of domains, influencing the point of view and behavior of their peers. Understanding individuals’ satisfaction is a key element for businesses, policy makers, organizations, and social institutions to make decisions. This has led to a growing amount of interest within the scientific community, and, as a result, to a host of new challenges that need to be solved. Sentiment analysis methodologies have been investigated and employed by researchers in the past to provide methodologies and resources to stakeholders. In the field of machine learning, deep learning models which combine several neural networks have emerged and have become the state-of-the-ar
t technologies in various domains for a variety of natural language processing tasks. The most prominent deep learning solutions are combined with word embeddings. However, how to include <!
strong>se
ntiment information</strong> in word-embedding representations to <strong>boost</strong> the performances of deep learning models, as well as <strong>explain</strong> what <strong>deep learning</strong> models (often employed as a black-box) learn are questions that still remain open and need further research and development.</span></p>
<p><span style="font-size: 13px;"><br></span></p>
<p><span style="font-size: 13px;">The investigation of these key points will answer to why and how design choices for creating embedding representations and designing deep learning should be made. This goes toward the direction of <strong>Explainable Deep Learning (XDL)</strong>, whose aim is to address how deep learning systems make decisions. This Special Issue aims to foster discussions about the design, development, and use of deep learning models and embedding representations which can help to improve state-of-the-art results, and at the same time enable interpreting and explaining the effectiveness of the use of deep learning for sentiment analysis. We invite theoretical works, implementations, and practical use cases that show benefits in the use of deep learning with a high focus o
n <strong>explainability</strong> for various domains.</span></p>
<p><span style="font-size: 13px;"><br></span></p>
<p><span style="font-size: 13px;"><strong><strong>-------------------------------------------------------</strong></strong></span></p>
<p><span style="font-size: 13px;"><strong>Topics<br></strong></span></p>
<p><span style="font-size: 13px;"><strong><strong>-------------------------------------------------------</strong></strong></span></p>
<p><span style="font-size: 13px;"><br></span></p>
<p><span style="font-size: 13px;">The Special Issue is focused but not limited to these topics:</span></p>
<ul>
<li><span style="font-size: 13px;">Deep learning topics</span>
<ul>
<li><span style="font-size: 13px;">Aspect-based DL and XDL models;</span></li>
<li><span style="font-size: 13px;">Bias detection within DL and XDL for sentiment analysis;</span></li>
<li><span style="font-size: 13px;">DL and XDL for toxicity and hate speech detection;</span></li>
<li><span style="font-size: 13px;">Multilingual DL and XDL for sentiment analysis;</span></li>
<li><span style="font-size: 13px;">DL and XDL for emotions detection;</span></li>
<li><span style="font-size: 13px;">Weak-supervised DL and XDL for sentiment analysis;</span></li>
<li><span style="font-size: 13px;">XDL design methodologies for sentiment analysis;</span></li>
<li><span style="font-size: 13px;">Analysis of DL models for sentiment analysis.</span></li>
</ul>
</li>
<li><span style="font-size: 13px;">Data representations topics</span>
<ul>
<li><span style="font-size: 13px;">Word embeddings for sentiment analysis;</span></li>
<li><span style="font-size: 13px;">Knowledge graph and knowledge graph embeddings for sentiment analysis;</span></li>
<li><span style="font-size: 13px;">Use of external knowledge (e.g., knowledge graphs) to feed DL for sentiment analysis;</span></li>
<li><span style="font-size: 13px;">Combination of existing sentiment analysis resources (e.g., SenticNet) with embedding representations;</span></li>
<li><span style="font-size: 13px;">Analysis of the performance of data representations for sentiment analysis tasks;</span></li>
<li><span style="font-size: 13px;">Lexicon-based explainability for sentiment analysis.</span></li>
</ul>
</li>
<li><span style="font-size: 13px;">Case studies</span>
<ul>
<li><span style="font-size: 13px;">Educational environments;</span></li>
<li><span style="font-size: 13px;">Healthcare systems;</span></li>
<li><span style="font-size: 13px;">Scholarly discussions (e.g., peer review process discussions, mailing lists, etc.);</span></li>
<li><span style="font-size: 13px;">News platforms;</span></li>
<li><span style="font-size: 13px;">Mental health systems;</span></li>
<li><span style="font-size: 13px;">Social networks.</span></li>
</ul>
</li>
</ul>
<p><span style="font-size: 13px;"><br></span></p>
<p><span style="font-size: 13px;"><strong>-------------------------------------------------------</strong></span></p>
<p><span style="font-size: 13px;"><strong>Deadline</strong></span></p>
<p><span style="font-size: 13px;"><strong>-------------------------------------------------------</strong></span></p>
<p><span style="font-size: 13px;"><br></span></p>
<p><span style="font-size: 13px;">Deadline for paper submission: Dec 31, 2021.</span></p>
<p><span style="font-size: 13px;"><strong>Papers submitted before the deadline will be reviewed upon receipt and published continuously in the journal as soon as accepted.</strong></span></p>
<p><span style="font-size: 13px;"><br></span></p>
<p><span style="font-size: 13px;"><strong>-------------------------------------------------------</strong></span></p>
<p><span style="font-size: 13px;"><strong>How to submit</strong></span></p>
<p><span style="font-size: 13px;"><strong>-------------------------------------------------------</strong></span></p>
<p><span style="font-size: 13px;"><br></span></p>
<p><span style="font-size: 13px;">Please use the Latex template: https://www.mdpi.com/authors/latex</span></p>
<p><span style="font-size: 13px;">Or Microsoft Word https://www.mdpi.com/files/word-templates/electronics-template.dot</span></p>
<p><span style="font-size: 13px;"><br></span></p>
<p><span style="font-size: 13px;">1. First-time users are required to register themselves before making submissions at http://susy.mdpi.com/</span></p>
<p><span style="font-size: 13px;">2. Enter your account and click "Submit Manuscript" under Submissions Menu.</span></p>
<p><span style="font-size: 13px;">3. Fill in manuscript details from Steps 1 to 4:</span></p>
<p><span style="font-size: 13px;">Journal: Electronics; Special Issue: Deep Learning and Explainability for Sentiment Analysis</span></p>
<p><span style="font-size: 13px;">4. Click the "submit" button after you finish all the steps.</span></p>
<p><span style="font-size: 13px;"><br></span></p>
<p><span style="font-size: 13px;"><strong>---------------------------------------------------------</strong></span></p>
<p><span style="font-size: 13px;"><strong>Guest Editors</strong></span></p>
<p><span style="font-size: 13px;"><strong>---------------------------------------------------------</strong></span></p>
<p><span style="font-size: 13px;"><br></span></p>
<p><span style="font-size: 13px;"><strong>Diego Reforgiato Recupero</strong></span></p>
<p><span style="font-size: 13px;">https://people.unica.it/diegoreforgiato/en/</span></p>
<p><span style="font-size: 13px;">Department of Mathematics and Computer Science</span></p>
<p><span style="font-size: 13px;">University of Cagliari</span></p>
<p><span style="font-size: 13px;">Email: [email protected]</span></p>
<p><span style="font-size: 13px;"><br></span></p>
<p><span style="font-size: 13px;"><strong>Harald Sack</strong></span></p>
<p><span style="font-size: 13px;">https://www.fiz-karlsruhe.de/en/forschung/lebenslauf-prof-dr-harald-sack</span></p>
<p><span style="font-size: 13px;">Information Service Engineering</span></p>
<p><span style="font-size: 13px;">FIZ- Karlsruhe Leibniz Institute for Information Infrastructure</span></p>
<p><span style="font-size: 13px;">Karlsruhe Institute of Technology (KIT) - Institute AIFB</span></p>
<p><span style="font-size: 13px;">Email: [email protected]</span></p>
<p><span style="font-size: 13px;"><br></span></p>
<p><span style="font-size: 13px;"><strong>Danilo Dessi'</strong></span></p>
<p><span style="font-size: 13px;">https://www.fiz-karlsruhe.de/en/forschung/lebenslauf-und-publikationen-dr-danilo-dessi</span></p>
<p><span style="font-size: 13px;">Information Service Engineering</span></p>
<p><span style="font-size: 13px;">FIZ- Karlsruhe Leibniz Institute for Information Infrastructure</span></p>
<p><span style="font-size: 13px;">Karlsruhe Institute of Technology (KIT) - Institute AIFB</span></p>
<p><span style="font-size: 13px;">Email: [email protected]</span></p>
<p><span style="font-size: 13px;"><br></span></p>
<p><span style="font-size: 13px;"><strong>-----------------------------------------------------------</strong></span></p>
<p><span style="font-size: 13px;"><strong>Contacts</strong></span></p>
<p><span style="font-size: 13px;"><strong>-----------------------------------------------------------</strong></span></p>
<p><span style="font-size: 13px;"><br></span></p>
<p><span style="font-size: 13px;">For general enquiries on the special issue, please send an email to [email protected]</span></p>
<p><span style="font-size: 13px;">For any questions regarding technical issues or the journal, please contact the assistant editor of this special issue: Mr. Eric Lin (E-mail: [email protected]).</span></p>
<p><span style="font-size: 13px;"><br></span></p>
<p><span style="font-size: 13px;"><br></span></p>
<p><span style="font-size: 13px;"><br></span></p>
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