Information Fusion for Affective Computing and Sentiment Analysis

  in Special Issue   Posted on July 19, 2019

Information for the Special Issue

Submission Deadline: Thu 31 Oct 2019
Journal Impact Factor : 5.667
Journal Name : Information Fusion
Journal Publisher:
Website for the Special Issue: https://www.journals.elsevier.com/information-fusion/call-for-papers/information-fusion-for-affective-computing
Journal & Submission Website: https://www.journals.elsevier.com/information-fusion

Special Issue Call for Papers:

Emotions are intrinsically a part of our mental activity and play a key role in cognitive communication and decision-making processes. Emotion is a chain of events made up of feedback loops. Feelings and behavior can affect cognition, just as cognition can influence feeling. Emotion, cognition, and action interact in feedback loops and emotion can be viewed in a structural model tied to adaptation. Besides being important for the advancement of AI, detecting and interpreting emotional information is key in multiple areas of computer science, e.g., human-agent, -computer, and -robot interaction, but also smart city e-learning, e-health, domotics (home automation), automotive and cyber security, user profiling and personalization etc.

In recent years, emotion and sentiment analysis has also become increasingly popular for processing social media data on social networks, online communities, blogs, Wikis, microblogging platforms, and other online collaborative media. The distillation of knowledge from such a big amount of unstructured information, however, is an extremely difficult task, as the contents of today’s Web are perfectly suitable for human consumption, but remain hardly accessible to machines. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open research challenges, as well as in the innovative business world, due to the remarkable benefits to be had from marketing and financial market prediction.

Most existing approaches to affective computing and sentiment analysis are still based on the syntactic representation of text, a method that relies mainly on word co-occurrence frequencies. Such algorithms are limited by the fact that they can only process information they can ‘see’. As human text processors, we do not have such limitations as every word we see activates a cascade of semantically related concepts, relevant episodes, emotions, and sensory experiences, all of which enable the completion of complex NLP tasks – such as word-sense disambiguation, textual entailment, and semantic role labeling – in a quick and effortless way.

Information fusion can aid to mimic the way humans process and analyze text and, hence, overcome the limitations of standard approaches to affective computing and sentiment analysis.

This special issue aims to provide a forum for academic and industrial communities to report recent advances in theoretical, experimental and integrative studies related to Information Fusion in Affective Computing and Sentiment Analysis – from the perspectives of algorithms, architectures, and applications. Articles are invited to address information fusion challenges in affective computing and sentiment analysis, across a range of interdisciplinary areas, such as machine learning, active learning, transfer learning, deep neural networks, neural and cognitive models, fuzzy logic, evolutionary computation, natural language processing, commonsense reasoning, and big data computing.

Manuscripts should cover unpublished research that clearly delineate the role of information fusion in the context of affective computing and sentiment analysis. Submitted manuscripts will be judged solely on the basis of new contributions excluding contributions made in earlier publications. Contributions should be described in sufficient detail to be reproducible on the basis of the material presented in the paper and the references cited therein.

Topics relevant to the Special Issue include, but are not necessarily limited to:

  • Concept-level sentiment analysis
  • Affective commonsense reasoning
  • Social network modeling and analysis
  • Social media representation and retrieval
  • Multi-lingual emotion and sentiment analysis
  • Aspect extraction for opinion mining
  • Linguistic patterns for sentiment analysis
  • Statistical learning theory for big social data analysis
  • Sarcasm detection
  • Microtext normalization
  • Sentic computing
  • Large commonsense graphs
  • Conceptual primitives for sentiment analysis
  • Multimodal emotion recognition and sentiment analysis
  • Human-agent, -computer, and -robot interaction
  • User profiling and personalization
  • Aided affective knowledge acquisition
  • Time-evolving sentiment tracking

Manuscripts should be submitted electronically online at http://ees.elsevier.com/inffus

The corresponding author will have to create a user profile if one has not been established before at Elsevier.

To ensure that all manuscripts are correctly identified for inclusion into the special issue, it is important that authors select “SI: InFus-ACSA\”.

Guest Editor(s)

Amir Hussain, Edinburgh Napier University, UK; E-mail: a.hussain@napier.ac.uk

Erik Cambria, NTU, Singapore; E-mail: cambria@ntu.edu.sg

Soujanya Poria, SUTD, Singapore; E-mail: sporia@sutd.edu.sg

Ahmad Hawalah, Taibah University, Saudi Arabia; E-mail: ahawalah@taibahu.edu.sa

Francisco Herrera, University of Grenada, Spain; E-mail: herrera@decsai.ugr.es

Deadline for Submission: October 31, 2019

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