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TITLE
Explainable Artificial Intelligence for Sentiment Analysis
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
JOURNAL
Knowledge-Based Systems (impact factor: 5.921)
CFP WEBLINK
sentic.net/xaisa.pdf
BACKGROUND AND MOTIVATION
Artificial-intelligence 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 artificial intelligence (XAI) is an emerging field in
machine learning that aims to address how artificial-intelligence
systems make decisions. It refers to artificial-intelligence
methods and techniques that produce human-comprehensible
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 Full length, original and unpublished research papers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
- XAI for sentiment and emotion analysis in social media
- XAI for aspect-based sentiment analysis
- XAI for multimodal sentiment analysis
- XAI for multilingual sentiment analysis
- XAI for conversational sentiment analysis
- Ante-hoc and post-hoc XAI approaches to sentiment analysis
- Semantic models for sentiment analysis
- Linguistic knowledge of deep neural networks for sentiment
analysis
- Explaining sentiment predictions
- Trust and interpretability in classification
- SenticNet 6 and other XAI-based knowledge bases for sentiment
analysis
- Sentic LSTM and other XAI-based deep nets for sentiment analysis
- Emotion categorization models for polarity detection
- Paraphrase detection in opinionated text
- Sarcasm and irony detection in online reviews
- Bias propagation and opinion diversity on online forums
- Opinion spam detection and intention mining
TIMEFRAME
Submission Deadline: 25th December 2020
Peer Review Due: 1st April 2021
Revision Due: 15th July 2021
Final Decision: 30th September 2021
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