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This work includes processing and classification of tweets which are written in Turkish language. Four different sector tweet datasets are vectorized with Word Embedding model and classified with Support Vector Machine and Random Forests classifiers and results have been compared. We have showed that sector based tweet classification is more successful compared to general tweets. Accuracy rates for...
The importance of learning important features in an automatic manner is growing exponentially as the volume of data and number of systems using pattern recognition techniques continue to increase. In this paper, arousal recognition from multi channels EEG signals was conducted using human crafted statistical features and learned features from 32 different EEG source channels. We have obtained 98.99%...
This paper addresses the task of political orientation prediction and assigning a person to one of ‘democrat’ or ‘republican’ classes based on Twitter data that is produced by republicans and democrat voters. We have used Long Short Term Memory Recurrent Neural Networks and Support Vector Machine, algorithms to model the classification process. Long Short Term Memory Recurrent Neural Networks performed...
Emotions play a significant and powerful role in everyday life of human beings. Developing algorithms for computers to recognize emotional expression is a widely studied area. In this study, emotion recognition from Galvanic signals was performed using time domain and wavelet based features. Feature extraction has been done with various feature set attributes. Various length windows have been used...
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