A deep learning approach to deal with data uncertainty in sentiment analysis

Sentiment Analysis refers to the process of computationally identifying and categorizing opinions expressed in a piece of text, in order to determine whether the writer’s attitude towards a particular topic or product is positive, negative, or even neutral. Recently, deep learning approaches emerge as powerful computational models that discover intricate semantic representations of texts automatically from data without hand-made feature engineering. These approaches have improved the state-of-the-art in many Sentiment Analysis tasks including sentiment classification of sentences or documents. In this paper we propose a semi-supervised neural network model, based on Deep Belief Networks, able to deal with data uncertainty for text sentences and adopting the Italian language as a reference language. We test this model against some datasets from literature related to movie reviews, adopting a vectorized representation of text and exploiting methods from Natural Language Processing (NLP) pre-processing.