Deep Learning for Sentiment Analysis

Teacher: Francesco Pugliese ( Istat)

In recent years, Sentiment Analysis (SA) has attracted significant attention in different areas of research and business applications. Indeed, “sentiments” (emotions, motivations) can influence the opinions of many relevant actors, such as product vendors, politicians, and the public opinion. Lately, Deep Learning (DL) models have been increasingly employed in SA thanks to their automatic high-dimensional feature extraction capabilities. In this module, we will understand and apply “Recurrent Convolutional Neural Networks” (RCNNs), that achieve “state-of-art” accuracy in sentiment classification, to some concrete problems. We will also see applications of “Recursive Neural Tensor Networks” (RNTNs) that reach an even better accuracy but require more pre-processing. We will also exploit an upside of Deep Learning, the possibility of extracting keywords from the final max-pooling layer of models to create a corpus of domain-specific keywords for different applications: from keywords-driven sentiment analysis to an extractive topic-summarization.