Includes but not limited to:
- Ontologies and knowledge bases for emotion recognition
- Topic and entity based emotion recognition
- Semantics in the evolution of emotions within and across social media systems and topics
- Semantic processing of social media for emotion recognition
- Contextualised emotion recognition
- Comparison of semantic approaches for emotion recognition
- Personalised semantic emotion recognition and monitoring
- Using semantics for prediction of emotions towards events, people, organisations, etc.
- Baselines and datasets for semantic emotion recognition
- Semantics in stream-based emotion recognition
- Comparison between semantic and non-semantic approaches for emotion recognition
- Multimodal emotion recognition
- Multilingual sentiment analysis
- Challenges in using semantics for emotion recognition
- Retrieval of emotion-based documents from repositories
- Deep learning and knowledge-enabled approaches for sentiment analysis
- Big Data tools and techniques for sentiment analysis
- Applications of sentiment analysis within specific domains (e.g. health, robotics)
