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 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 emotionannotated 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 1 .

Comparison Between Traditional Machine Learning Models And Neural Network Models For Vietnamese Hate Speech Detection

“…Text-CNN: Convolutional neural network (CNN) is a multistage Neural network architecture developed for classification. By using convolutional layers, it can detect combination features [7], [20]. In our experiments, we use 4 convolutional layers with 32 filters for each layer.…”