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Detail projektu
Období řešení: 01.04.2015 — 31.03.2017
Zdroje financování
Evropská unie - Horizon 2020
- plně financující (2015-04-01 - 2017-03-31)
O projektu
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
Popis českyProjekt vyvine aplikace pro analýzu velkých multilingválních a multimodálních dat se zaměřením na vytváření emočních profilů chování uživatelů. Bude využito kombinace textových zdrojů, audio/video (včetně analýzy mluvené řeči v několika jazycích), sociální sítě a strukturovaná data.
Klíčová slovasocial semantics, multilingual multi-modal emotion analysis, knowledge graphs, Social TV, Brand Reputation Management, Call Centre Operations
Klíčová slova českysociální sémantika, vícejazyčná vícemodální emoční analýza, znalostní grafy, sociální televize
Originální jazyk
angličtina
Řešitelé
Smrž Pavel, doc. RNDr., Ph.D. - hlavní řešitelČernocký Jan, prof. Dr. Ing. - spoluřešitelDytrych Jaroslav, Ing., Ph.D. - spoluřešitelMatějka Jiří, Ing. - spoluřešitelNedeljković Sava, Bc. - spoluřešitelOtrusina Lubomír, Ing. - spoluřešitelPrexta Dávid, Bc. - spoluřešitelRusiňák Petr, Ing. - spoluřešitelSuchánek Jan, Ing. - spoluřešitelŠvaňa Miloš, Bc. - spoluřešitelZapletal Jakub, Ing. - spoluřešitelZárybnický Jakub, Ing. - spoluřešitel
Útvary
Ústav počítačové grafiky a multimédií- příjemce (08.10.2014 - 31.03.2017)
Výsledky
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
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
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
HRADIŠ, M.; KOHÚT, J.: EmotionService; Video emotion web service. http://www.fit.vutbr.cz/~ihradis/data/EmotionService-1.0-rc1.tar.bzhttp://www.fit.vutbr.cz/~ihradis/prods.php?id=527¬itle=1. URL: http://www.fit.vutbr.cz/~ihradis/data/EmotionService-1.0-rc1.tar.bzhttp://www.fit.vutbr.cz/~ihradis/prods.php?id=527¬itle=1. (software)Detail
DYTRYCH, J.; KOUŘIL, J.; KARÁSEK, M.; SMRŽ, P.; DOLEŽAL, J.; OTRUSINA, L.: corpproc; Corpora Processing Software. http://knot.fit.vutbr.cz/corpproc/. URL: http://knot.fit.vutbr.cz/corpproc/. (software)Detail
OTRUSINA, L.; SMRŽ, P.: BLOGS4ME; Blogs downloader for MixedEmotions project. http://www.fit.vutbr.cz/research/prod/index.php?id=481. URL: http://www.fit.vutbr.cz/research/prod/index.php?id=481. (software)Detail
DOLEŽAL, J.; SMRŽ, P.; DYTRYCH, J.; OTRUSINA, L.; KOUŘIL, J.: SEC; Semantic Enrichment Component. http://sec.fit.vutbr.cz/. URL: http://sec.fit.vutbr.cz/. (software)Detail
Odkaz
http://www.mixedemotions-project.eu