A Linked Data Model for Multimodal Sentiment and Emotion Analysis

Abstract

The number of tools and services for sentiment analysis is increasing rapidly. Unfortunately, the lack of standard formats hinders interoperability. To tackle this problem, previous works propose the use of the NLP Interchange Format (NIF) as both a common semantic format and an API for textual sentiment analysis. However, that approach creates a gap between textual and sentiment analysis that hampers multimodality. This paper presents a multimedia extension of NIF that can be leveraged for multimodal applications. The application of this extended model is illustrated with a service that annotates online videos with their sentiment and the use of SPARQL to retrieve results for different modes.