Browsing Research Institutes and Centres by Author "Andryushechkin, Vladimir"

Now showing items 1-4 of 4

    • A comparison of emotion annotation approaches for text 

      Wood, Ian D.; McCrae, John P.; Andryushechkin, Vladimir; Buitelaar, Paul (MDPI, 2018-05-11)
      While the recognition of positive/negative sentiment in text is an established task with many standard data sets and well developed methodologies, the recognition of a more nuanced affect has received less attention: there ...

    • A comparison of emotion annotation schemes and a new annotated data set 

      Wood, Ian D.; McCrae, John P.; Andryushechkin, Vladimir; Buitelaar, Paul (European Languages Resources Association (ELRA), 2018-05-07)
      While the recognition of positive/negative sentiment in text is an established task with many standard data sets and well developed methodologies, the recognition of more nuanced affect has received less attention, and ...

    • MixedEmotions: An open-source toolbox for multi-modal emotion analysis 

      Buitelaar, Paul; Wood, Ian D.; Negi, Sapna; Arcan, Mihael; McCrae, John P.; Abele, Andrejs; Robin, Cécile; Andryushechkin, Vladimir; Ziad, Housam; Sagha, Hesam; Schmitt, Maximilian; Schuller, Björn W.; Sánchez-Rada, J. Fernando; Iglesias, Carlos A.; Navarro, Carlos; Giefer, Andreas; Heise, Nicolaus; Masucci, Vincenzo; Danza, Francesco A.; Caterino, Ciro; Smrž, Pavel; Hradiš, Michal; Povolný, Filip; Klimeš, Marek; Matějka, Pavel; Tummarello, Giovanni (IEEE, 2018-01-25)
      Recently, there is an increasing tendency to embed the functionality of recognizing emotions from the user generated contents, to infer richer profile about the users or contents, that can be used for various automated ...

    • NUIG at EmoInt-2017: BiLSTM and SVR ensemble to detect emotion intensity 

      Andryushechkin, Vladimir; Wood, Ian D.; O'Neill, James (Association for Computational Linguistics, 2017-09-08)
      This paper describes the entry NUIG in the WASSA 20171 shared task on emotion recognition. The NUIG system used an SVR (SVM regression) and BiLSTM ensemble, utilizing primarily n-grams (for SVR features) and tweet word ...