Sentiment analysis has been a very popular technique in recent times for analyzing a person’s behavior. It mainly deals with the process of computationally identifying and categorizing opinions expressed in the form of text, especially to determine the writer’s attitude towards a topic. Categories involved while classifying these sentiments are positive, negative or neutral. In addition to sentiment analysis, some of the other techniques like facial expressions, pupil dilation has also been widely used. Nevertheless, this proves that numerous attempts have been made towards analyzing a person’s perspective, but their overall effectiveness has been on the lower side. The reason behind their inadequate approach is straightforward; user or subject can easily trick the machine by writing fake or sentimental text, also he or she can make false facial expressions to give wrong signals which hence will be interpreted incorrectly. So, instead of analyzing these outer expressions and signals, if we could directly examine the data produced by the brain and inspect its underlying patterns, then that might give us more accurate results. The emphasis of this thesis is to use such brain signals to classify and uncover the underlying emotions in a human brain. The two targeted classes of emotion are valence and arousal. We will be using deep learning models in the form of artificial neural net and convolution neural net to process the electroencephalography or brain waves data and map it to these two classes. The learning approach will be of supervised learning as corresponding labels are given with the training data. The approach ultimately yields a validation score of more than 75% for one and close to 65% for the second, which is acceptable with the amount of data we had for the training of these deep learning models.
|Commitee:||Tankelevich, Roman, Hoffman, Michael|
|School:||California State University, Long Beach|
|Department:||Computer Engineering and Computer Science|
|School Location:||United States -- California|
|Source:||MAI 81/1(E), Masters Abstracts International|
|Subjects:||Computer science, Artificial intelligence|
|Keywords:||Convolution neural nets, DEAP dataset, Deep learning, Deep neural nets, Emotion recognition, Machine learning|
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