Sentiment Detection through Emotion Classification Using Deep Learning Approach for Chinese Text

Abstract

Emotion classification and sentiment analysis represent crucial research areas within the field of Natural Language Processing. Previous studies have primarily focused on conducting sentiment classification and emotion classification as separate tasks. Only a limited number of researchers have delved into exploring the relationship between these two and invested efforts in deriving one from the other. This study aims to determine sentiment by employing emotion classifications. Specifically, we utilise the ERNIE Tiny deep learning model to classify emotions in Chinese texts, and detect sentiments through our devised rules. For instance, if emotions such as ‘happiness' or ‘like’ are present, the sentiment is classified as positive. Conversely, emotions like ‘sadness', ‘disgust’, ‘anger’, or ‘fear’ classify the sentiment as negative. The experimental results demonstrate the F1 score of 93.00% and 90.14% for positive and negative sentiment, respectively, in Chinese song reviews. These findings substantiate the validity and feasibility of utilising emotions to extract sentiment