EMOTION BASED 2D PLAYLIST PERSUASION SYSTEM

EMOTION BASED 2D PLAYLIST PERSUASION SYSTEM
Author(s):
Mrs. Aruna kumari, R. Swarna Bharathi, Sk. Nazeera Tunnisa, Y. Varsha, V. Hemanth
Keywords:
emotion, random forest algorithm, music recommendation, NN classifier, features extraction , Decision tree.
Abstract
In this paper mainly focused on emotion based music recommendation system. The project presents text emotion recognition from text signal based on features analysis and NN-classifier. Automatic Text emotion recognition plays an important role in HCI systems for measuring people’s emotions (i.e., anger, disgust, fear, happiness, sadness, and surprise). The recognition system involves Text emotion detection, features extraction and selection and finally classification. These features are useful to distinguish the maximum number of samples accurately and the NN classifier based on discriminant analysis is used to classify the six different expressions. The simulated results will be shown that the filter-based feature extraction with used classifier gives much better accuracy with lesser algorithmic complexity than other Text emotion expression recognition approaches. The results indicate about the optimal accuracy for Random forest are 92.3% respectively.
Article Details
Unique Paper ID: 159106 Publication Volume & Issue: Volume 9, Issue 11 Page(s): 431 - 434
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