Emotionally Intelligent Music Player for Mood Improvement based on Text Emotion Recognition using Deep Learning Approach
Keywords:
Deep learning, Machine learning, Lexicon database, BBC database, Convolutional neural network, Multiclass Support Vector Machine.Abstract
This study shows a unique approach of Mood elevating music player based on Text emotion recognition. Emotions play a very vital role in everyday life. In this internet era, textual data is mainly designed for communication. Natural language processing is designed for textual data such as messages, emails, articles, reviews, posts, etc. Sentiment analysis is used in various fields. For emotion recognition from text, Deep learning with machine learning approach is used. CNN(Convolutional Neural Network) with a multiclass support vector machine algorithm is used. One vs Rest approach is used for multiclass SVM classifier. Lexicon database and BBC database are operated. Proposed system is compared with K-nearest Neighbour (KNN), Random Forest (RF), Naïve Bayes (NB) algorithms. Results show the accuracy of around 86.88% using BBC database with and approximately 91.2% using Lexicon database, which is higher than other classifiers. Other classifiers such as Random Forest (RF) shows the accuracy of 68.44% for lexicon and 62.44% for BBC, Naïve Bayes (NB) shows the accuracy of 62.56% for lexicon and 59.28% for BBC, K-nearest neighbour (KNN) shows the accuracy of 74.12% for lexicon and 69.28% for BBC. As a result, CNN with multiclass SVM gives 91.2% accuracy using lexicon database.
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