Affiliations: [a] Department of Computer Science and Engineering, Shri Mata Vaishnov Devi University, Katra, Jammu and Kashmir, India
| [b] Department of Computer Science and Engineering, Shri Mata Vaishnov Devi University, Katra, Jammu and Kashmir, India
Correspondence:
[*]
Corresponding author. Sushil Kumar Trisal, Department of Computer Science and Engineering, Shri Mata Vaishnov Devi University, Katra, Jammu and Kashmir, India. Tel.: +91 01991 285524, +91 09419931212; Fax: +91 0191 2470067. E-mail: sushil.trisal@smvdu.ac.in.
Abstract: Social networks or social media is an online platform for billons of people around the world. This platform makes it easier for the people to have conversations, share information, share videos, instant messaging, create virtual world and more. The most dominant form of interaction on social media is by the text messaging. To detect the emotions from these text messages is not a difficult job for the humans as they are linked with emotions themselves. But to detect the emotions from these text messages by the computer is a difficult job to perform. Various models like fuzzy model, vector space model, keystroke dynamics, character n-gram models etc have been proposed in the literature for the detection of emotions but every model has its limitations and drawbacks. In this study a novel K-RCC (Reduced Computational Complexity) emotion detection model is proposed which is based on the K Nearest Neighbor (KNN) algorithm. The K-RCC algorithm reduces the computational complexity and incorrect classification rate which is the main drawback of the KNN algorithm. The computational complexity of the KNN algorithm is reduced up to some extent by the K-d Tree algorithm but on the cost of increased incorrect classification rate. The systematic performance analysis of K-RCC is carried out with four Machine learning classification algorithms for the detection of human emotions from tweets collected from social media site twitter. The emotions are classified under six emotional classes such as disgust, fear, joy, sadness, anger, and shame. The K-RCC performs better both in terms of reducing the computational complexity and incorrect classification rate and detection of human emotions.
Keywords: Affective computing, cognitive process, emotions, human behavior, machine learning, social media.
