Year of Graduation
Deep Learning for Sentiment Analysis of Short Texts
Lately, deep learning techniques are widely used for solving problems of supervised learning. This is also true for sentiment analysis tasks. The goal of this research is to construct an architecture of the model for classification of short texts, namely, aggressive comments in the Internet based on the type of their toxicity. This work demonstrates that, in this task, deep learning models significantly outperform popular linear methods of machine learning. Also it is shown that due to the short average length of texts, such approaches as TF-IDF and convolutional layers prove ineffective. Finally, a combination of Bidirectional LSTM with Attention layer on top of it is proposed and implemented. The result of the model is 0.98 in AUC-ROC.