Predicting Emotion Labels for Chinese Microblog Texts

We describe an experiment into detecting emotions in texts on the Chinese microblog service Sina Weibo (www.weibo.com) using distant supervision via various author-supplied emotion labels (emoticons and smilies). Existing word segmentation tools proved unreliable; better accuracy was achieved using character-based features. Higher-order n-grams proved to be useful features. Accuracy varied according to label and emotion: while smilies are used more often, emoticons are more reliable. Happiness is the most accurately predicted emotion, with accuracies around 90 % on both distant and gold-standard labels. This approach works well and achieves high accuracies for happiness and anger, while it is less effective for sadness, surprise, disgust and fear, which are also difficult for human annotators to detect.