Algorithms For Automatic Labelling Of Text Data

The aim of this project was to create a semi-supervised learning model in order to perform automatic labeling of text data. Initially, I studied the related work in the field of text mining in order to be able to construct and conduct my methodology. Secondly, I explored several supervised learning algorithms in order to train a classifier that can recognize the relevant from the irrelevant Tweets derived from Twitter data about breathing problems. Furthermore I created a semi-supervised learning model based on self-learning and on one of the models I had already trained and examined the possibility of augmenting the training set by adding the successful predictions.