Emotion Recognition for Vietnamese Social Media Text
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 the 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.
Cite
@article{arxiv.1911.09339,
title = {Emotion Recognition for Vietnamese Social Media Text},
author = {Vong Anh Ho and Duong Huynh-Cong Nguyen and Danh Hoang Nguyen and Linh Thi-Van Pham and Duc-Vu Nguyen and Kiet Van Nguyen and Ngan Luu-Thuy Nguyen},
journal= {arXiv preprint arXiv:1911.09339},
year = {2020}
}
Comments
PACLING 2019