Text analysis with deep learning and data augmentation

With the vast amount of textual data available on the Web, it is becoming increasingly difficult to analyze them manually. Therefore, there is a growing need to automatically process them for various applications such as opinion mining, sentiment classification, and question answering to name but a few. While traditional text analysis techniques such as N-gram language models can perform reasonably well, they still rely on manual feature engineering. Deep neural networks do away with manually designing features and allow us to create systems with the capability of end-to-end data processing. In order to do this effectively, they depend heavily on the amount of input data for training. However, the data can still be scarce for applications or domains that are newly worked on. In these cases, data augmentation techniques can be used to augment the input data to help networks perform better. In this dissertation, we make several contributions to text analysis by addressing some of its problems including Sentiment Analysis (SA), Toxic Language Detection (TLD), Text Classification (TC). Firstly, we introduce a novel deep architecture to address Aspect-Based Sentiment Analysis (ABSA), combining adversarial training, which is a form of data augmentation in the embedding space, with a state-of-the-art pre-trained language model called BERT. Then, we propose two additive modules that are attached on top of BERT and help improve the model performance. Furthermore, we introduce a simple bag-of-words model which performs reasonably well in detecting toxic language despite its simplicity. Moreover, we put forward a novel data augmentation technique in the input space, and show that it is fruitful for neural network models applied on various text classification data sets. Finally, collecting product image and comments from social media, we build an annotated multimodal dataset that can be utilized to address Aspect-Based Emotion Analysis (ABEA).