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Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection.

opinion mining --- affect computing --- health insurance --- Twitter --- hybrid vectorization --- violence against women --- word association --- collaborative schemes of sentiment analysis and sentiment systems --- random forest --- cyber-aggression --- deep learning --- online review --- emotion analysis --- lexicon construction --- provider networks --- text mining --- sentiment lexicon --- social media --- sentiment-aware word embedding --- psychographic segmentation --- medical web forum --- gender classification --- racism --- sentiment analysis --- sentiment classification --- sentiment word analysis --- social networks --- convolutional neural network --- review data mining --- machine learning --- emotion classification --- big data-driven marketing --- text feature representation --- recommender system --- user preference prediction --- violence based on sexual orientation --- semantic networks