Sentiment analysis using convolutional neural network with fastText embeddings

Convolution Neural Networks (CNNs) are famous for their great performance in Computer Vision experiments achieving state-of-art results. However, recent works have shown that CNNs can perform well for Natural Language Processing. The whole idea consists of gathering the embeddings as an image. This paper presents the usage of the recently released Facebook fastText word embeddings as representation of word to perform the task of sentiment analysis. The interest in this work comes from the advent of social media and technological advances, which have been flooding the Internet with opinions. The results show that the proposed aproach outperforms the baseline models and it has similar performance to the state-of-art models.