Special Issue Information Special Issue Call for Paper Other Special Issues on this journal Closed Special Issues
Explainable Artificial Intelligence for Sentiment Analysis

Explainable Artificial Intelligence for Sentiment Analysis

Impact Score 11.71


Special Issue Information

Submission Deadline: 25-12-2020
Journal Impact Score: 11.71
Journal Name: Knowledge-Based Systems
Publisher: Knowledge-Based Systems

Special Issue Call for Papers

Aims & Scope

Social media analytics have proven valuable in numerous research areas as a pragmatic tool for public opinion mining and analysis. Sentiment analysis addresses the dynamics of complex socio-affective applications that permeate intelligence and decision making in the sentient and solution-savvy Social Web.

Having started as simple polarity detection, contemporary sentiment analysis has advanced to a more nuanced analysis of affect and emotion sensing. Detecting finegrained sentiment in natural language, however, is tricky even for humans, making its automated detection very complicated. Moreover, online opinions can be put forth in the form of text reviews or ratings, for a product as a whole, or each of its individual aspects. Multiple and lengthy reviews, usage of casual dialect with microtext (wordplay, neologism and slang), use of figurative language (sarcasm, irony), multilingual content (code-mixed and code-switched) and opinion spamming add challenges to the task of extracting opinions.

Recently memes, GIFs, typo-graphic (artistic way of text representation), info-graphic (text embedded along with an image) visual content and edited videos also dominate social feeds. Consequently, the intra-modal modeling and inter-modal interactions between the textual, visual and acoustic components add to the linguistic challenges. Therefore, conceptualization and development of multi-faceted sentiment analysis models to adequately capture observed opinion-sensitive information are imperative.

Artificial intelligence (AI) driven models, especially deep learning models, have achieved state-of-the-art results for various natural language processing tasks including sentiment analysis. We get highly accurate predictions using these in conjunction with large datasets, but with little understanding of the internal features and representations of the data that a model uses to classify into sentiment categories. Most techniques do not disclose how and why decisions are taken. In other words, these black-box algorithms lack transparency and explainability.

Explainable AI (XAI) is an emerging field in machine learning that aims to address how AI systems make decisions. It refers to AI methods and techniques that produce humancomprehensible solutions. XAI solutions will enable enhanced prediction accuracy with decision understanding and traceability of actions taken. XAI aims to improve human understanding, determine the justifiability of decisions made by the machine, introduce trust and reduce bias.

This special issue aims to stimulate discussion on the design, use and evaluation of XAI models as the key knowledge-discovery drivers to recognize, interpret, process and simulate human emotion for various sentiment analysis tasks. We invite theoretical work and review articles on practical use-cases of XAI that discuss adding a layer of interpretability and trust to powerful algorithms such as neural networks, ensemble methods including random forests for delivering near real-time intelligence. 

Concurrently, works on social computing, emotion recognition and affective computing research methods which help mediate, understand and analyze aspects of social behaviors, interactions, and affective states based on observable actions are also encouraged. Full length, original and unpublished research papers based on theoretical or experimental contributions related to understanding, visualizing and interpreting deep learning models for sentiment analysis and interpretable machine learning for sentiment analysis are also welcome.

Topics of interest include, but are not limited to:

● XAI for sentiment and emotion analysis in social media
● Ante-hoc and post-hoc approaches of XAI for sentiment analysis
● Semantic models for sentiment analysis
● Linguistic knowledge of deep neural networks for sentiment analysis
● Explaining sentiment predictions
● Trust and interpretability in classification
● Knowledge representation methods for opinions
● Aspect-based sentiment analysis
● Multimodal sentiment analysis
● Multilingual aspects of sentiment analysis
● Affective computing and Opinion mining
● Emotion analysis in conversational data
● Natural language processing and XAI
● Paraphrase detection in opinionated text
● Sarcasm and irony detection in online reviews
● Bias propagation and opinion diversity on online forums
● Opinion spamming and intention mining

Important Dates

● Submission Deadline: 25th December 2020
● Peer Review Due: 1st April 2021
● Revision Due: 15th July 2021
● Final Decision: 30th September 2021

Dissemination, Composition and Review Procedures

The CFP will be published online in August-2020.

● A call for papers will be circulated to invite submissions.
● World leading researchers will be invited as guest authors.
● As an effort to attract contributors from around, the call for papers will be advertised across sentic channels, and different websites, distribution lists, conferences, associations, etc.

This special issue will run as per the timeline given from submission to publication, while maintaining the rigorous peer review and high standards of the journal. All manuscripts submitted must be original, not under consideration elsewhere, and not previously published. A guide for authors and other relevant information for submission of manuscripts is available on the instructions for the author's page. Authors can expect their manuscripts to be reviewed fairly, and in a skilled, conscientious manner. To enhance objectivity, and to guarantee high scientific quality and relevance to the subject,
three peer reviewers will be selected to evaluate a manuscript.

The peer review process shall be designed to avoid bias and conflict of interest on the part of reviewers and shall be composed of experts in the relevant field of research. A key criterion in publication decisions will be the manuscript’s fit for the special issue and the readership of KBS. Papers will be published online as soon as accepted in a continuous flow. The final edition is scheduled for publication in April 2021.

Guest Editors
Erik Cambria, Nanyang Technological University, Singapore
Akshi Kumar, Delhi Technological University, India
Mahmoud Al-Ayyoub, Jordan University of Science and Technology, Jordan
Newton Howard, Oxford University, UK

Recent Special Issues Organized by the Guest Editors

● Soft Computing for Recommender Systems and Sentiment Analysis, Special Issue of Applied Soft Computing (2019) https://sentic.net/rssa.pdf
● Information Fusion for Affective Computing and Sentiment Analysis, Special Issue of Information Fusion (2019) https://sentic.net/ifacsa.pdf
● Social Media Marketing and Financial Forecasting, Special Issue of Information Processing & Management (2019) https://sentic.net/smmff.pdf
● Data Science in Social Media, Special Issue of Future Generation of Computer Systems (2019) https://sentic.net/dssm.pdf
● Bridging Cognitive Models and Recommender Systems, Special Issue of Cognitive Computation (2019) https://sentic.net/cmrs.pdf
● Computational Intelligence for Affective Computing and Sentiment Analysis, Special Issue of IEEE Computational Intelligence (2019) https://sentic.net/ciacsa.pdf
● Misinformation, Fake-news and Rumour detection in Low-Resource Languages, Special Issue of ACM Transactions on Asian and Low-Resource Language Information Processing (2020) https://dl.acm.org/pb-assets/static_journal_pages/tallip/pdf/tallip-si-cfp-05-2020-1591128335640.pdf
● Soft Computing Models for Big Data and Internet of Things (IoT), Special Issue of Big Data, Mary Ann Liebert, Inc publishers. (2020) https://home.liebertpub.com/cfp/special-issue-on-soft-computing-models-forbig-data-and-inte/259/
● Artificial Intelligence driven Big Data Analytics: Techniques and Trends, Special Issue of Autosoft Journal (Intelligent Automation & Soft Computing) (2020). http://wacong.org/autosoft/auto/pdf/CFP_SI_on_Big_Data.pdf
● Computational Intelligence for Internet of Everything, Special Issue of IEEE Internet of Things Magazine (2020)
● Social, Mobile, Analytic and Cloud (SMAC) Technologies: Intelligent computing for future smart cities, Special Issue of Sustainable Cities & Society, Elsevier (2020) https://www.journals.elsevier.com/sustainable-cities-and-society/call-forpapers/special-issue-on-social-mobile-analytic-and-cloud
● Recent Advances in Information and Communication Technologies, Special Issue of Future Internet, MDPI (2020)
● Energy Efficiency in Building using Intelligent computing for Smart cities, Special Issue of Energy Systems, Springer (2019) https://static.springer.com/sgw/documents/1654731/application/pdf/Energy+Efficiency+in+Building+2019.pdf

Guest Editors’ Brief Biographies

Erik Cambria is an Associate Professor at NTU, where he also holds the appointment of Provost Chair in Computer Science and Engineering. He earned his PhD through a joint programme between the University of Stirling and MIT Media Lab. Erik is recipient of many awards, e.g., the 2018 AI's 10 to Watch and the 2019 IEEE Outstanding Early Career award, and is often featured in the news, e.g., Forbes. He is Associate Editor of several journals, e.g., INFFUS, IEEE CIM, and KBS, Special Content Editor of FGCS, Department Editor of IEEE Intelligent Systems, Editorial Board Chair of Cognitive
Computation, and is involved in many international conferences as area/program chair.

Akshi Kumar is an Assistant Professor in the Department of Computer Science & Engineering at Delhi Technological University. She received her Ph.D. in Computer Engineering from Faculty of Technology, University of Delhi, India in 2011. She is a recipient of “commendable research award for excellence in research” at Delhi Technological University, in 2018 and 2019 and "VIWA Outstanding Women in Engineering (Computer Science & Engineering)" Award, in 2020. Dr. Kumar has guided several doctoral and post-graduate scholars. She also has two Indian Patents published. She is an active reviewer of many top journals published by IEEE, IET, Springer and Elsevier. In addition, she has served as a Guest Editor of IEEE and Springer journals. Her research interests are in the area of Affective Computing, Sentiment Analysis, Rumour
Analytics, Artificial Intelligence and Natural Language Understanding.

Mahmoud Al-Ayyoub received his Ph.D. in Computer Science (CS) from Stony Brook University in 2010. He is a CS professor at the Jordan University of Science and Technology, Jordan. This year (2019/2020), he is taking a sabbatical leave, which he is spending at the Electrical and Electronic Engineering Department at the University of Manchester, UK. His research interests include Machine Learning and its applications in Natural Language Processing and Computer Vision.

Newton Howard is a brain and cognitive scientist, the former Director of the MIT Mind Machine Project at the Massachusetts Institute of Technology (MIT). He is a Professor of Computational Neuroscience and Functional Neurosurgery at the University of Oxford, where he directs the Oxford Computational Neuroscience Laboratory. He is also the Director of MIT's Synthetic Intelligence Lab, the founder of the Center for Advanced Defense Studies and the Chairman of the Brain Sciences Foundation. Professor Howard is also a Senior Fellow at the John Radcliffe Hospital at Oxford, a Senior Scientist at INSERM in Paris and a P.A.H. at the CHU Hospital in Martinique. His research areas
include Cognition, Memory, Trauma, Machine Learning, Comprehensive Brain Modeling, Natural Language Processing, Nanotech, Medical Devices and Artificial Intelligence.

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