- Prof. Erik Cambria, NTU, Singapore (email@example.com)
- Dr. Lorenzo Malandri, POLIMI, Italy (firstname.lastname@example.org)
Background and Motivation
Emotions are intrinsically part of our mental activity and play a key role in 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 e-learning, e-health, domotics, automotive, security, user profiling and personalization.
In recent years, emotion and sentiment analysis has become increasingly popular also 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 challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial market prediction.
Most of 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. Computational data and social networks 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.
Articles are thus invited in 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. Topics include, but are not 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
- Learning word dependencies in text
- 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
- Natural language based financial forecasting
- Regular papers: 12 pages
- Extended Abstract (work in progress) : 2 pages
The workshop will accept original research contribution, review paper, and survey paper. Page limit is including the references and appendices.
If you do not want to publish the extended abstract and only want to present it, please mention it in the extended abstract and if possible, please also email this to the organizers.
- Papers must be formatted using the LNCS format.
- Submissions are open on EasyChair.
- More details are available at CSoNet Website.
- Accepted papers will be published in proceedings Lecture Notes in Computer Science and will be indexed by ISI (CPCI-S, included in ISI Web of Science), EI Engineering Index (Compendex and Inspec databases), ACM Digital Library, DBLP, Google Scholar, MathSciNet and many more.
- Selected high quality papers will be invited for publications in Journal of Combinatorial Optimization, IEEE Transactions on Network Science and Engineering, and Computational Social Networks.
- Each accepted paper needs at least one full registration, before the camera-ready manuscript can be included in the proceedings.
- Registration fee details are available at CSoNet 2019 Website.
- Paper Submission Deadline: August 24, 2019
- Acceptance Notification: September 24, 2019
- Camera Ready & Registration: October 01, 2019
- Conference Dates: November 18-20, 2019