Abstract
Deep emotion recognition is the central objective of our recent research efforts. This study examines the capability of several deep learning architectures and word embeddings to classify emotions on two Twitter datasets. We have identified several aspects worth investigating that appeared to challenge and contrast previously established notion that semantic information is captured by distributional word representations. Our evidence has shown that extending the word embeddings to account for the use of emojis and incorporating a suitable lexicon of emotional words can lead to a better classification of the emotional content carried by Twitter messages.
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Notes
- 1.
https://competitions.codalab.org/competitions/17751#learn_the_details-datasets, last accessed: May 2018.
- 2.
https://data.world/crowdflower/sentiment-analysis-in-text, last accessed: May 2018.
- 3.
https://www.nltk.org/, last accessed: May 2018.
- 4.
https://nlp.stanford.edu/projects/glove/, last accessed: May 2018.
- 5.
https://github.com/felipebravom/AffectiveTweets/releases/tag/1.0.0, last accessed: May 2018.
- 6.
https://www.cs.waikato.ac.nz/ml/sa/files/, last accessed: May 2018.
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This work was partially financed by the Faculty of Computer Science and Engineering at the “Ss. Cyril and Methodius” University.
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Stojanovska, F., Toshevska, M., Gievska, S. (2018). Explorations into Deep Neural Models for Emotion Recognition. In: Kalajdziski, S., Ackovska, N. (eds) ICT Innovations 2018. Engineering and Life Sciences. ICT 2018. Communications in Computer and Information Science, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-00825-3_19
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