The 1st Workshop on Sentiment Analysis and Emotion Recognition aims to enable synergy among key aspects, such as how to manage the sensory capacity of robots to gather the information needed, how to improve machine learning techniques to be suitable for social robots, and how to manage different modalities with efficient fusion methods. This workshop provides a venue for members from a range of international institutions, including universities, research labs, and industry to exchange ideas and experiences, analyze, present, and discuss latest research and development issues, and propose theoretical foundations related to research on combining solutions for developing efficient and suitable solutions for emotion and sentiment detection for social robots. This synergistic focus will enforce the engineering of intelligent and innovative solutions in this regard to guide their design and to promote the development of new approaches (architectures, tools, and models) of emotion/sentiment recognition for social robots.
Univ. Católica San Pablo, Peru
Univ. Simón Bolívar, Venezuela
Univ. de Valparaíso, Chile
Univ. Católica San Pablo, Peru
Univ. Bordeaux - ESTIA, France
February 25th, 2022; March 8th, 2022; March 15th, 2022
Notification of Acceptance:
April 4th, 2022; April 8th, 2022
April 18th, 2022; April 20th, 2022
Prospective authors are encouraged to submit papers for evaluation by the Program Committee. All submissions will be peer-reviewed by at least 3 peer reviewers with expertise in the area. This process will result in constructive feedback to the authors and the selection of the best contributions to be presented in the workshop and published in the proceedings. After the preliminary notification date, authors rebut by evidence and arguments all reviewer inquiries and their comments. Based on the rebuttal feedback, reviewers notify authors with the final decision. Selection criteria will include: relevance, significance, impact, originality, technical soundness, and quality of presentation. Preference will be given to submissions that take strong or challenging positions on important emergent topics related to the workshop. At least one author should attend the workshop to present the paper.
All papers accepted for publication must follow the formatting rules for Springer Proceedings (https://www.iospress.com/book-article-instructions) and be written in English, with a length of at least 6 but no more than 10 pages. Latex and Word templates can be found in http://www.iospress.nl/service/authors/latex-and-word-tools-for-book-authors.
Important note for double blind review policy: The version of papers for evaluation by the Program Committee, saved in PDF format, must not include identification, e-mail, affiliation of the authors, grants, funding institution or any explicit information, that may disclose the authors’ identity (this information is to be restored in the camera-ready version upon acceptance). Please remove author names and affiliations (or replace it with Xs) on submitted papers. In particular, in the version submitted for review please avoid explicit auto-references, such as “as we shown in ” — consider “as shown in “. i.e., you may cite your own previous works provided that it is not deducible from the text that the cited work belongs to the authors. This information must only be available in the camera-ready version of accepted papers, saved in Word or Latex format and also in PDF format. These files must be accompanied by the Consent to Publish form filled out, in a ZIP file, and uploaded at the conference management system.
The submission system for this workshop is based on EasyChair. To submit or upload a paper please go to https://easychair.org/conferences/?conf=sentirobots2022.
All papers accepted in the Workshops program will be published as a volume of the Ambient Intelligence and Smart Environments Series of IOS Press and electronically available through ACM Digital Library (pending approval). The proceedings will be ISI indexed.
Erik Cambria is the Founder of SenticNet, a Singapore-based company offering B2B sentiment analysis services, and an Associate Professor at NTU, where he also holds the appointment of Provost Chair in Computer Science and Engineering. Prior to joining NTU, he worked at Microsoft Research Asia (Beijing) and HP Labs India (Bangalore) and earned his PhD through a joint programme between the University of Stirling and MIT Media Lab. His research focuses on neurosymbolic AI for explainable natural language processing in domains like sentiment analysis, dialogue systems, and financial forecasting. He is recipient of several awards, e.g., IEEE Outstanding Career Award, was listed among the AI's 10 to Watch, and was featured in Forbes as one of the 5 People Building Our AI Future. He is Associate Editor of many top AI journals, e.g., INFFUS, IEEE CIM, and AIRE, Department Editor of IEEE Intelligent Systems (ACSA), and is involved in various international conferences.
With the recent developments of deep learning, AI research has gained new vigor and prominence. However, machine learning still faces three big challenges: (1) it requires a lot of training data and is domain-dependent; (2) different types of training or parameter tweaking leads to inconsistent results; (3) the use of black-box algorithms makes the reasoning process uninterpretable. At SenticNet, we address such issues in the context of NLP via sentic computing, a neurosymbolic approach that aims to bridge the gap between statistical NLP and the many other disciplines necessary for understanding human language such as linguistics, commonsense reasoning, and affective computing. Sentic computing is both top-down and bottom-up: top-down because it leverages symbolic models such as semantic networks and conceptual dependency representations to encode meaning; bottom-up because it uses subsymbolic methods such as deep neural networks and multiple kernel learning to infer syntactic patterns from data.