Tutorial: "Using Sentiment Analysis as a Case Study for Introducing Modern NLP Concepts" Felipe Bravo (PhD, University of Waikato)
Resumen: Sentiment analysis is the task of automatically classifying text into sentiment categories such as positive and negative. Until 2014, state-of-the-art solutions to this problem relied on shallow learning schemes based on hand-crafted features and linear machine learning models. Deep neural networks architectures had became very popular in the computer vision community due to its success for detecting objects (“cat”,“bicycles”) regardless of its position in the image. These approaches have also been recently adopted for many natural language processing (NLP) tasks, including sentiment analysis, with successful results. In this tutorial, we use sentiment analysis as a case study for introducing modern neural network architectures with a focus on sentiment analysis. These architectures include word embeddings, convolution neural networks, and recurrent neural networks.