Abstract
Emotion recognition or emotion prediction is a higher approach or a special case of sentiment analysis. In this task, the result is not produced in terms of either polarity: positive or negative or in the form of rating (from 1 to 5) but of a more detailed level of analysis in which the results are depicted in more expressions like sadness, enjoyment, anger, disgust, fear and surprise. Emotion recognition plays a critical role in measuring brand value of a product by recognizing specific emotions of customers’ comments. In this study, we have achieved two targets. First and foremost, we built a standard Vietnamese Social Media Emotion Corpus (UIT-VSMEC) with exactly 6,927 emotion-annotated sentences, contributing to emotion recognition research in Vietnamese which is a low-resource language in natural language processing (NLP). Secondly, we assessed and measured machine learning and deep neural network models on our UIT-VSMEC corpus. As a result, the CNN model achieved the highest performance with the weighted F1-score of 59.74%. Our corpus is available at our research website (https://sites.google.com/uit.edu.vn/uit-nlp/corpora-projects).
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Acknowledgment
We would like to give our thanks to the NLP@UIT research group and the Citynow-UIT Laboratory of the University of Information Technology - Vietnam National University Ho Chi Minh City for their supports with pragmatic and inspiring advice.
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Ho, V.A. et al. (2020). Emotion Recognition for Vietnamese Social Media Text. In: Nguyen, LM., Phan, XH., Hasida, K., Tojo, S. (eds) Computational Linguistics. PACLING 2019. Communications in Computer and Information Science, vol 1215. Springer, Singapore. https://doi.org/10.1007/978-981-15-6168-9_27
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DOI: https://doi.org/10.1007/978-981-15-6168-9_27
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