A Comparative Study of Various Classifiers for Emotion Detection from Text

  • Akhilesh Kumar Singh, Krishn Kant, Darshika Srivastava, Dhruv Goel

Abstract

Emotion detection from text has become more popular in recent times. The reason behind its large scale use is its enormous potential applications in human-computer interaction marketing, psychology, political science, artificial intelligence, etc. Emotion is a way of expressing the thoughts of something that is one of the hardest work to find. A person's real emotion through his / her text. From the human face, we can analyze the present state of that person. Every human being will express their feeling/emotion implicitly or explicitly by their gestures, facial expressions, text, and speech. In this paper, we analyze text through many techniques introduced in the past, through which emotion can be detected. Here, we are working on four i.e. Linear Regression, SVM, Random Forest, Naive Byes methods using the TF-IDF technique to detect the emotions.

Published
2020-05-20
How to Cite
Akhilesh Kumar Singh, Krishn Kant, Darshika Srivastava, Dhruv Goel. (2020). A Comparative Study of Various Classifiers for Emotion Detection from Text. International Journal of Advanced Science and Technology, 29(06), 6262 - 6270. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/19912