Affective Computing and Sentiment Analysis

The Department of IEEE Intelligent Systems on Affective Computing and Sentiment Analysis (ACSA) focuses on the introduction, presentation, and discussion of novel techniques that further develop and apply affective reasoning tools and techniques for emotion detection and sentiment analysis in different modalities.

Affective Computing and Sentiment Analysis

A key motivation for this Department is to explore the adoption of novel affective reasoning frameworks and cognitive learning systems to go beyond a mere word-level analysis of natural language text and provide novel concept-level tools and techniques that allow a more efficient passage from (unstructured) natural language to (structured) machine-processable affective data, in potentially any domain.

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.

Articles are thus invited in areas such as machine learning, semi-supervised learning, active learning, transfer learning, deep neural networks, neural and cognitive models, data mining, pattern recognition, knowledge-based systems, information retrieval, 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
• Deep learning for sarcasm detection
• 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
The Department also welcomes papers on specific application domains of affective computing and analysis, e.g., influence networks, customer experience management, intelligent user interfaces, multimedia management, computer-mediated human-human communication, enterprise feedback management, surveillance, art. The authors will be required to follow the Author’s Guide for manuscript submission to IEEE Intelligent Systems.

The IEEE Intelligent Systems Department on Affective Computing and Sentiment Analysis consists of papers on novel methods and techniques that further develop and apply affective reasoning tools and techniques for polarity and emotion detection in different modalities. Articles are relatively short (about 3,000 words maximum - each figure/table counting for 200 words - and no more than a dozen citations) and will be mostly by invitation (but unsolicited submissions are welcome too). There is no template for the article but the format shall be Microsoft Word. Some papers may survey various aspects of the topic: the balance between these will be adjusted to maximize the Department’s impact. All articles are expected to successfully negotiate the standard review procedures for IEEE Intelligent Systems. Enquiries and submissions shall be sent to

• N Majumder, S Poria, A Gelbukh, E Cambria. Deep learning based document modeling for personality detection from text. IEEE Intelligent Systems 32(2) (2017)

• A Bandhakavi, N Wiratunga, S Massie, P Deepak. Lexicon generation for emotion analysis of text. IEEE Intelligent Systems 32(1), pp. 102-108 (2017)

• A Zadeh, R Zellers, E Pincus, LP Morency. Multimodal sentiment intensity analysis in videos: Facial gestures and verbal messages. IEEE Intelligent Systems 31(6), pp. 82-88 (2016)

• R Mihalcea, A Garimella. What men say, what women hear: Finding gender-specific meaning shades. IEEE Intelligent Systems 31(4), pp. 62-67 (2016)

• E Cambria. Affective computing and sentiment analysis. IEEE Intelligent Systems 31(2), pp. 102-107 (2016)

• Erik Cambria, Nanyang Technological University (Singapore)