Project Details
Social Semantic Emotion Analysis for Innovative Multilingual Big Data Analytics Markets
Project Period: 1. 4. 2015 – 31. 3. 2017
Project Type: grant
Agency: Evropská unie
Program: Horizon 2020
social semantics, multilingual multi-modal emotion analysis, knowledge graphs,
Social TV, Brand Reputation Management, Call Centre Operations
Emotion analysis is central to tracking customer and user behaviour and
satisfaction, which can be observed from user interaction in the form of explicit
feedback through email, call center interaction, social media comments, etc., as
well as implicit acknowledgement of approval or rejection through facial
expressions, speech or other non-verbal feedback. In Europe specifically, but
increasingly also globally, an added factor here is that user feedback can be in
multiple languages, in text as well as in speech and audio-visual content. This
implies different cultural backgrounds and thus different ways to produce and
perceive emotions in everyday interactions, beyond the fact of having specific
rules for encoding and decoding emotions in each language. Making sense of
accumulated user interaction from different data sources, modalities and
languages is challenging and has not yet been explored in fullness in an
industrial context. Commercial solutions exist but do not address the
multilingual aspect in a robust and large-scale setting and do not scale up to
huge data volumes that need to be processed, or the integration of emotion
analysis observations across data sources and/or modalities on a meaningful
level, i.e. keeping track of entities involved as well the connections between
them (who said what? to whom? in the context of which event, product, service?)
In MixedEmotions we will implement an integrated Big Linked Data platform for
emotion analysis across heterogeneous data sources, languages and modalities,
building on existing state of the art tools, services and approaches that will
enable the tracking of emotional aspects of user interaction and feedback on an
entity level. The MixedEmotions platform will provide an integrated solution for
Large-scale emotion analysis and fusion on heterogeneous, multilingual, text,
speech, video and social media data streams, leveraging open access and
proprietary data sources, exploiting also social context by leveraging social
network graphs Semantic-level emotion information aggregation and
integration through robust extraction of social semantic knowledge graphs for
emotion analysis along multidimensional clusters The platform will be developed
and evaluated in the context of three cross-domain Pilot Projects that are
representative of a variety of data analytics markets: Social TV, Brand
Reputation Management, Call Centre Operations. Each of the companies involved in
the pilot projects have specific innovation objectives
Černocký Jan, prof. Dr. Ing. (DCGM)
Dytrych Jaroslav, Ing., Ph.D. (DCGM)
Matějka Jiří, Ing.
Nedeljković Sava, Bc.
Otrusina Lubomír, Ing. (DCGM)
Prexta Dávid, Bc.
Rusiňák Petr, Ing.
Suchánek Jan, Ing.
Švaňa Miloš, Bc.
Zapletal Jakub, Ing.
Zárybnický Jakub, Ing.
Zemčík Pavel, prof. Dr. Ing., dr. h. c. (DCGM)
2018
- BUITELAAR, P.; WOOD, I.; NEGI, S.; ARCAN, M.; MCCRAE, J.; ABELE, A.; ROBIN, C.; ANDRYUSHECHKIN, V.; ZIAD, H.; SAGHA, H.; SCHMITT, M.; SCHULLER, B.; SÁNCHEZ-RADA, J.; IGLESIAS, C.; NAVARRO, C.; GIEFER, A.; HEISE, N.; MASUCCI, V.; DANZA, F.; CATERINO, C.; SMRŽ, P.; HRADIŠ, M.; POVOLNÝ, F.; KLIMEŠ, M.; MATĚJKA, P.; TUMMARELLO, G. MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis. IEEE TRANSACTIONS ON MULTIMEDIA, 2018, vol. 20, no. 9,
p. 2454-2465. ISSN: 1520-9210. Detail - DYTRYCH, J.; SMRŽ, P. Advanced User Interfaces for Semantic Annotation of Complex Relations in Text. In Agents and Artificial Intelligence. Lecture Notes in Computer Science. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2018.
p. 205-221. ISBN: 978-3-319-93581-2. ISSN: 0302-9743. Detail
2016
- DYTRYCH, J.; SMRŽ, P. Interaction Patterns in Computer-assisted Semantic Annotation of Text - An Empirical Evaluation. In Proceedings of the 8th International Conference on Agents and Artificial Intelligence. Volume 2: ICAART. Setúbal: SciTePress - Science and Technology Publications, 2016.
p. 74-84. ISBN: 978-989-758-172-4. Detail - MACHÁČEK, J. BUTknot at SemEval-2016 task 5: Supervised machine learning with term substitution approach in Aspect Category Detection. In SemEval 2016 - 10th International Workshop on Semantic Evaluation. San Diego: Association for Computational Linguistics, 2016.
p. 301-305. ISBN: 978-1-941643-95-2. Detail - OTRUSINA, L.; SMRŽ, P. WTF-LOD - A New Resource for Large-Scale NER Evaluation. In Proceedings of the Tenth conference on International Language Resources and Evaluation (LREC'16). Portorož: European Language Resources Association, 2016.
p. 3299-3302. ISBN: 978-2-9517408-9-1. Detail - POLOK, L.; ILA, V.; SMRŽ, P. 3D Reconstruction Quality Analysis and Its Acceleration on GPU Clusters. In Proceedings of European Signal Processing Conference 2016. Budapest: Institute of Electrical and Electronics Engineers, 2016.
p. 1108-1112. ISBN: 978-0-9928626-6-4. Detail - POLOK, L.; SMRŽ, P. Increasing Double Precision Throughput on NVIDIA Maxwell GPUs. In Proceedings of the 24th High Performance Computing Symposium. Pasadena / Los Angeles: Association for Computing Machinery, 2016.
p. 146-153. ISBN: 978-1-5108-2318-1. Detail - POPKOVÁ, A.; POVOLNÝ, F.; MATĚJKA, P.; GLEMBEK, O.; GRÉZL, F.; ČERNOCKÝ, J. Investigation of Bottle-Neck Features for Emotion Recognition. In 19th International Conference, TSD 2016, Brno , Czech Republic, September 12-16, 2016, Proceedings. Lecture Notes in Computer Science. Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence. Brno: International Speech Communication Association, 2016.
p. 426-434. ISSN: 0302-9743. Detail - POVOLNÝ, F.; MATĚJKA, P.; HRADIŠ, M.; POPKOVÁ, A.; OTRUSINA, L.; SMRŽ, P.; WOOD, I.; ROBIN, C.; LAMEL, L. Multimodal Emotion Recognition for AVEC 2016 Challenge. In AVEC '16 Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge. Amsterdam: Association for Computing Machinery, 2016.
p. 75-82. ISBN: 978-1-4503-4516-3. Detail - SAGHA, H.; MATĚJKA, P.; GAVRYUOKOVA, M.; POVOLNÝ, F.; MARCHI, E.; SCHULLER, B. Enhancing multilingual recognition of emotion in speech by language identification. In 17TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION - Proceedings (INTERSPEECH 2016). Proceedings of Interspeech. San Francisco: International Speech Communication Association, 2016.
p. 2949-2953. ISSN: 1990-9772. Detail