#emotion classification

[antiopesayingthings]

On Moral Emotions

source:

[ijtsrd]

EEG Based Classification of Emotions with CNN and RNN

by S. Harshitha | Mrs. A. Selvarani ""EEG Based Classification of Emotions with CNN and RNN""

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020,

URL: https://www.ijtsrd.com/papers/ijtsrd30374.pdf

Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30374/eeg-based-classification-of-emotions-with-cnn-and-rnn/s-harshitha

ugclistedjournals, indexedjournal, paperpublication

Emotions are biological states associated with the nervous system, especially the brain brought on by neurophysiological changes. They variously cognate with thoughts, feelings, behavioural responses, and a degree of pleasure or displeasure and it exists everywhere in daily life. It is a significant research topic in the development of artificial intelligence to evaluate human behaviour that are primarily based on emotions. In this paper, Deep Learning Classifiers will be applied to SJTU Emotion EEG Dataset SEED to classify human emotions from EEG using Python. Then the accuracy of respective classifiers that is, the performance of emotion classification using Convolutional Neural Network CNN and Recurrent Neural Networks are compared. The experimental results show that RNN is better than CNN in solving sequence prediction problems. 

[munishmaxtech]

Sentiment analysis and opinion mining in social networks present nowadays a hot topic of research. However, most of the state of the artworks and researches on the automatic sentiment analysis and opinion mining of texts collected from social networks and microblogging websites are oriented toward the binary classification (i.e., classification into “positive”and “negative”) or the ternary classification (i.e., classification into “positive,”“negative,”and “neutral”) of texts. In this paper, we propose a novel approach that, in addition to the aforementioned tasks of binary and ternary classifications, goes deeper in the classification of texts collected from Twitter and classifies these texts into multiple sentiment classes. While in this paper, we limit our scope to approx ten different sentiment classes.