X-SENTIMENT 2021 : 6th International Workshop on eXplainable SENTIment Mining and EmotioN deTection

Hersonissos, Greece
Event Date: Jun 06, 2021 - Jun 07, 2021
Submission Deadline: Mar 01, 2021
Notification of Acceptance: Mar 31, 2021
Camera Ready Version Due : Apr 09, 2021


As the Web rapidly evolves, people are becoming increasingly enthusiastic about interacting, sharing, and collaborating through social networks, online communities, blogs, wikis, therefore, it is critical to enable the correct interpretation of the sentiments and opinions expressed or reported about social events, political movements, company strategies, marketing campaigns, and any other form or online interaction. Models driven by sub-symbolic Artificial Intelligence such as machine learning algorithms and vector representations have achieved state-of-the-art results for Sentiment Analysis tasks. These models get very accurate results but with a limited understanding of patterns and features used to correctly classify into sentiment categories. Thus, these models lack transparency, traceability, and explainability on how the decisions are taken. This limits Artificial Intelligence methods to produce human-comprehensible solutions, reducing the trust towards the machine generated results. Within this scenario, semantic technologies with explicit semantics can be leveraged to explain why a resource has been classified in a specific sentiment category, inducing trustworthy and avoiding biases.

Call For Paper

We are interested in novel contributions to explain Sentiment Analysis results through the use and development of methodologies based on semantics, focused but not limited to the following areas. We seek to receive papers that clearly state and contextualize how the proposed contribution is integrated in the real-world scenario and supports all the stakeholders in using and applying Sentiment Analysis.

  • Methodologies

    • Automatic methods to build ontologies for Sentiment Analysis.
    • Automatic methods to build knowledge graphs for Sentiment Analysis.
    • Automatic methods to build lexicons for Sentiment Analysis.
    • Explainable Machine Learning models for Sentiment Analysis.
    • Bias detection through semantics for Sentiment Analysis.
    • Semantics for toxicity and hate speech detection.
    • Application of existing semantic technologies multimedia data.
    • Multilingual Sentiment Analysis methodologies.
  • Representation Methods

    • Combination of existing resources (e.g., SenticNet) with embedding representations.
    • Novel Word Embeddings for Sentiment Analysis.
    • Knowledge Graph Embeddings for Sentiment Analysis.
    • Reviews about existing limitations.
  • Case Study Exploration

    • Educational environments.
    • Healthcare systems.
    • Scholarly discussions (e.g., peer review process discussions, mailing lists, etc.).
    • Mental health systems.
    • News platforms.
    • Social networks.

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