In this bachelor thesis, I first introduce the machine learning methodology of text classification with the goal to describe the functioning of neural networks. Then, I identify and discuss the current development of Convolutional Neural Networks and Recurrent Neural Networks from a text classification perspective and compare both models. Furthermore, I introduce different techniques used to translate textual information in a language comprehensible by the computer, which ultimately serve as inputs for the models previously discussed. From there, I propose a method for the models to cope with words absent from a training corpus. This first part has also the goal to facilitate the access to the machine learning world to a broader audience than computer science students and experts. To test the proposal, I implement and compare two state-of-the-art models and eight different word representations using pre-trained vectors on a dataset given by LogMeIn and on a common benchmark.
I find that, with my configuration, Convolutional Neural Networks are easier to train and are also yielding better results. Nevertheless, I highlight that models that combine both architectures can potentially have a better performance, but need more work on identifying appropriate hyperparameters for training. Finally, I find that the efficacy of word embedding methods depends not only on the dataset but also on the model used to tackle the subsequent task. In my context, they can boost performance by up to 10.2% compared to a random initialization. However, further investigations are necessary to evaluate the value of my proposal with a corpus that contains a greater ratio of unknown relevant words.