Title :
Classifying emotion in Thai youtube comments
Author :
Sarakit, Phakhawat ; Theeramunkong, Thanaruk ; Haruechaiyasak, Choochart ; Okumura, Manabu
Author_Institution :
Sch. of ICT, Thammasat Univ., Pathumthani, Thailand
Abstract :
To add more value on YouTube, a popular portal of social media clips, it is worth recognizing automatically the mood of a media clip using the comments given to such clip. This paper presents a method to classify emotion of a Thai media clip on YouTube using the comments given to the clip. Six basic emotions considered are Anger, Disgust, Fear, Happiness, Sadness and Surprise. Performances using three alternative machine learning algorithms, namely multinomial naïve Bayes (MNB), decision tree (DT) and support vector machine (SVM) are compared. As the result, SVM achieves the highest accuracy in the commercial advertisement (AD) genre with an accuracy of 76.14% while MNB with yields the best result in the music video (MV) genre with an accuracy of 84.48%.
Keywords :
Bayes methods; decision trees; emotion recognition; human computer interaction; learning (artificial intelligence); pattern classification; portals; social networking (online); support vector machines; AD genre; DT; MNB; MV genre; SVM; Thai YouTube comments; Thai media clip; anger; commercial advertisement genre; decision tree; disgust; emotion classification; fear; happiness; machine learning algorithms; media clip mood; multinomial naïve Bayes; music video genre; portal; sadness; social media clips; support vector machine; surprise; Accuracy; Conferences; Media; Sentiment analysis; Support vector machines; Twitter; YouTube; Emotion Classification; Emotion Detection; Machine Learning; Text Analysis;
Conference_Titel :
Information and Communication Technology for Embedded Systems (IC-ICTES), 2015 6th International Conference of
Conference_Location :
Hua-Hin
DOI :
10.1109/ICTEmSys.2015.7110808
