A Linked Data Model for Multimodal Sentiment and Emotion Analysis

Sánchez-Rada, J. Fernando; Iglesias, Carlos Á.; Gil, Ronald

doi:10.18653/v1/w15-4202

Cited by 8 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Earlier works propose a linked data approach to solve these aspects [3,4], but the concepts and tooling behind ontologies and linked data publishing are unfamiliar to both the linguistic community and developers. Senpy aims to bridge this gap by presenting a tool that creates full-fledged services that benefit from the power of semantic technologies without requiring any prior knowledge of web or semantic technologies.…”

Section: Problems and Backgroundmentioning

confidence: 99%

Senpy: A framework for semantic sentiment and emotion analysis services

Sánchez-Rada

Araque

Iglesias

2020

Knowledge-Based Systems

Self Cite

View full text Add to dashboard Cite

Section: Problems and Backgroundmentioning

confidence: 99%

Senpy: A framework for semantic sentiment and emotion analysis services

Sánchez-Rada

Araque

Iglesias

2020

Knowledge-Based Systems

Self Cite

View full text Add to dashboard Cite

“…To this end, texts are converted to RDF 19 literals and a URI 20 is generated so that linked data annotations can be defined for that text. The same idea can also be applied to annotate multimedia [92]. The combination of Onyx's meta-models of emotion with the homogeneous multimedia annotation can be leveraged for automatic conversion and fusion of multimodal results [93].…”

Section: Linked Data and Knowledge Graphmentioning

confidence: 99%

MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis

Buitelaar

Wood

Negi

et al. 2018

IEEE Trans. Multimedia

Self Cite

View full text Add to dashboard Cite

Recently, there is an increasing tendency to embed functionalities for recognizing emotions from user generated media content in automated systems such as call-centre operations, recommendations and assistive technologies, providing richer and more informative user and content profiles. However, to date, adding these functionalities was a tedious, costly, and time consuming effort, requiring identification and integration of diverse tools with diverse interfaces as required by the use case at hand. The MixedEmotions Toolbox leverages the need for such functionalities by providing tools for text, audio, video, and linked data processing within an easily integrable plug-and-play platform. These functionalities include: (i) for text processing: emotion and sentiment recognition, (ii) for audio processing: emotion, age, and gender recognition, (iii) for video processing: face detection and tracking, emotion recognition, facial landmark localization, head pose estimation, face alignment, and body pose estimation, and (iv) for linked data: knowledge graph integration. Moreover, the MixedEmotions Toolbox is open-source and free. In this article, we present this toolbox in the context of the existing landscape, and provide a range of detailed benchmarks on standard test-beds showing its state-of-the-art performance. Furthermore, three real-world use-cases show its effectiveness, namely emotion-driven smart TV, call center monitoring, and brand reputation analysis.

show abstract

“…Another important contribution laid the foundation to multimodal annotation [23] by adding a multimedia extension to the NIF model. The bulk of this extension (MESA) is the addition of a URI scheme for multimedia contexts, which complement the original NIF string context.…”

Section: Multimodal Linked Data Approachesmentioning

confidence: 99%

Multimodal multimodel emotion analysis as linked data

Sánchez-Rada

Iglesias

Sagha

et al. 2017

2017 Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)

Self Cite

View full text Add to dashboard Cite

The lack of a standard emotion representation model hinders emotion analysis due to the incompatibility of annotation formats and models from different sources, tools and annotation services. This is also a limiting factor for multimodal analysis, since recognition services from different modalities (audio, video, text) tend to have different representation models (e. g., continuous vs. discrete emotions). This work presents a multidisciplinary effort to alleviate this problem by formalizing conversion between emotion models. The specific contributions are: i) a semantic representation of emotion conversion; ii) an API proposal for services that perform automatic conversion; iii) a reference implementation of such a service; and iv) validation of the proposal through use cases that integrate different emotion models and service providers.

show abstract

A Linked Data Model for Multimodal Sentiment and Emotion Analysis

Cited by 8 publications

References 14 publications

Senpy: A framework for semantic sentiment and emotion analysis services

Senpy: A framework for semantic sentiment and emotion analysis services

MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis

Multimodal multimodel emotion analysis as linked data

Contact Info

Product

Resources

About