IEEE ACSA


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 multiple modalities and different languages.   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.

RATIONALE
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.

TOPICS
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.

COMPOSITION PROCEDURES
The IEEE Intelligent Systems Department on Affective Computing and Sentiment Analysis consists of short papers on novel methods and techniques that further develop and apply affective reasoning tools and techniques for polarity and emotion detection in different modalities. ACSA columns are mostly by invitation but unsolicited submissions are welcome too, as long as they link well with ACSA literature. Articles shall be submitted using the ACSA LaTex template, contain 10 references tops, and be no longer than 8 pages, including text, tables, figures, and author biographies (no appendices allowed). All articles shall be authored by maximum 5 researchers and are expected to successfully negotiate the standard review procedures for IEEE Intelligent Systems. Enquiries and submissions shall be sent to dept_acronym@sentic.net

Affective Computing and Sentiment Analysis

ACSA COLUMNS
• M Nadeem et al. Are Foundation Models the Next-Generation Social Media Content Moderators?. IEEE Intelligent Systems 39(6) (2024)

• X Zhang et al. Joint Weakly-supervised Image Emotion Analysis Based on Inter-class Discrimination and Intra-class Correlation. IEEE Intelligent Systems 39(5), 82-89 (2024)

• G Tu et al. AdaCLF: An Adaptive Curriculum Learning Framework for Emotional Support Conversation. IEEE Intelligent Systems 39(4), 5-11 (2024)

• P Kazienko and E Cambria. Towards Responsible Recommender Systems. IEEE Intelligent Systems 39(3), 5-12 (2024)

• M Anas et al. Can Generative AI Models Extract Deeper Sentiments as Compared to Traditional Deep Learning Algorithms? IEEE Intelligent Systems 39(2), 5-10 (2024)

• M Amin et al. Can ChatGPT's Responses Boost Traditional Natural Language Processing? IEEE Intelligent Systems 38(5), 5-11 (2023)

• M Kulakowski and F Frasincar. Sentiment Classification of Cryptocurrency-related Social Media Posts. IEEE Intelligent Systems 38(4), 5-9 (2023)

• V La Gatta et al. Covid-19 Sentiment Analysis Based on Tweets. IEEE Intelligent Systems 38(3), 51-55 (2023)

• M Amin et al. Will Affective Computing Emerge from Foundation Models and General Artificial Intelligence? A First Evaluation on ChatGPT. IEEE Intelligent Systems 38(2), 15-23 (2023)

• S Dmello and B Booth. Affect Detection from Wearables in the “Real” Wild: Fact, Fantasy, or Somewhere Inbetween? IEEE Intelligent Systems 38(1), 76-84 (2023)

• J Löchner and B Schuller. Child & Youth Affective Computing – Challenge Accepted. IEEE Intelligent Systems 37(6), 69-76 (2022)

• H Shi et al. Multi-scale 3D Shift Graph Convolution Network for Emotion Recognition from Human Actions. IEEE Intelligent Systems 37(4), 103-110 (2022)

• M Dragoni et al. OntoSenticNet 2: Enhancing Reasoning within Sentiment Analysis. IEEE Intelligent Systems 37(2), 103-110 (2022)

• R Chiong et al. Combining Sentiment Lexicons and Content-based Features for Depression Detection. IEEE Intelligent Systems 36(6), 99-105 (2021)

• M Thelwall. This! Identifying New Sentiment Slang through Orthographic Pleonasm Online: Yasss Slay Gorg Queen Ilysm. IEEE Intelligent Systems 36(4), 114-120 (2021)

• W Peng et al. Adaptive Modality Distillation for Separable Multimodal Sentiment Analysis. IEEE Intelligent Systems 36(3), 82-89 (2021)

• L Stappen et al. Sentiment Analysis and Topic Recognition in Video Transcriptions. IEEE Intelligent Systems 36(2), 88-95 (2021)

• Q Jiang et al. Towards Aspect-level Sentiment Modification without Parallel Data. IEEE Intelligent Systems 36(1), 75-81 (2021)

• E Ragusa et al. Image Polarity Detection on Resource-constrained Devices. IEEE Intelligent Systems 35(6), 50-57 (2020)

• Y Susanto et al. The Hourglass Model Revisited. IEEE Intelligent Systems 35(5), 96-102 (2020)

• JC Du et al. Commonsense Knowledge Enhanced Memory Network for Stance Classification. IEEE Intelligent Systems 35(4), 102-109 (2020)

• A Esuli et al. Cross-lingual Sentiment Quantification. IEEE Intelligent Systems 35(3), 106-114 (2020)

• JW Bi et al. crowd Intelligence: Conducting Asymmetric Impact-performance Analysis Based on Online Reviews. IEEE Intelligent Systems 35(2), 92-98 (2020)

• J Schuurmans and F Frasincar. Intent Classification for Dialogue Utterances. IEEE Intelligent Systems 35(1), 82-88 (2020)

• A Buker et al. Type Like A Man! Inferring Gender from Keystroke Dynamics in Live-chats. IEEE Intelligent Systems 34(6), 53-59 (2019)

• SA Qureshi et al. Multitask Representation Learning for Multimodal Estimation of Depression Level. IEEE Intelligent Systems 34(5), 45-52 (2019)

• C Welch et al. Learning from Personal Longitudinal Dialog Data. IEEE Intelligent Systems 34(4), 16-23 (2019)

• N Majumder et al. Sentiment and Sarcasm Classification with Multitask Learning. IEEE Intelligent Systems 34(3), 38-43 (2019)

• J Reis et al. Supervised Learning for Fake News Detection. IEEE Intelligent Systems 34(2), 76-81 (2019)

• QJ Yang et al. Segment-level Joint Topic-sentiment Model for Online Review Analysis. IEEE Intelligent Systems 34(1), 43-50 (2019)

• S Poria et al. Multimodal Sentiment Analysis: Addressing Key Issues and Setting Up the Baselines. IEEE Intelligent Systems 33(6), 17-25 (2018)

• MS Akhtar et al. No, That Never Happened!! Investigating Rumors on Twitter. IEEE Intelligent Systems 33(5), 8-15 (2018)

• D Mahata et al. Detecting Personal Intake of Medicine from Twitter. IEEE Intelligent Systems 33(4), 87-95 (2018)

• M Dragoni et al. OntoSenticNet: A Commonsense Ontology for Sentiment Analysis. IEEE Intelligent Systems 33(3), 77-85 (2018)

• F Xu et al. Instance-based Domain Adaptation via Multi-clustering Logistic Approximation. IEEE Intelligent Systems 33(1), 78-88 (2018)

• E Cambria et al. Sentiment Analysis is a Big Suitcase. IEEE Intelligent Systems 32(6), 74-80 (2017)

• M Ebrahimi et al. Challenges of Sentiment Analysis for Dynamic Events. IEEE Intelligent Systems 32(5), 70-75 (2017)

• A Valdivia et al. Sentiment Analysis in TripAdvisor. IEEE Intelligent Systems 32(4), 72-77 (2017)

• A Weichselbraun et al. Aspect-based Extraction and Analysis of Affective Knowledge from Social Media Streams. IEEE Intelligent Systems 32(3), 80-88 (2017)

• N Majumder et al. Deep Learning-based Document Modeling for Personality Detection from Text. IEEE Intelligent Systems 32(2), 74-79 (2017)

• A Bandhakavi et al. Lexicon Generation for Emotion Analysis from Text. IEEE Intelligent Systems 32(1), 102-108 (2017)

• A Zadeh et al. Multimodal Sentiment Intensity Analysis in Videos: Facial Gestures and Verbal Messages. IEEE Intelligent Systems 31(6), 82-88 (2016)

• R Mihalcea and A Garimella. What Men Say, What Women Hear: Finding Gender-specific Meaning Shades. IEEE Intelligent Systems 31(4), 62-67 (2016)

• E Cambria. Affective Computing and Sentiment Analysis. IEEE Intelligent Systems 31(2), 102-107 (2016)