TY - BOOK ID - 64931140 TI - Sentiment Analysis for Social Media AU - Moreno, Antonio AU - Iglesias, Carlos A. PY - 2020 SN - 3039285734 3039285726 PB - MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - opinion mining KW - affect computing KW - health insurance KW - Twitter KW - hybrid vectorization KW - violence against women KW - word association KW - collaborative schemes of sentiment analysis and sentiment systems KW - random forest KW - cyber-aggression KW - deep learning KW - online review KW - emotion analysis KW - lexicon construction KW - provider networks KW - text mining KW - sentiment lexicon KW - social media KW - sentiment-aware word embedding KW - psychographic segmentation KW - medical web forum KW - gender classification KW - racism KW - sentiment analysis KW - sentiment classification KW - sentiment word analysis KW - social networks KW - convolutional neural network KW - review data mining KW - machine learning KW - emotion classification KW - big data-driven marketing KW - text feature representation KW - recommender system KW - user preference prediction KW - violence based on sexual orientation KW - semantic networks UR - https://www.unicat.be/uniCat?func=search&query=sysid:64931140 AB - 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. ER -