Sentiment Analysis for Social MediaCarlos A. Iglesias, Antonio Moreno 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. |
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accessed accuracy algorithm applied approach associated authors behavior better binary classification cluster combination communication compared Computing Conference construct consumer context CrossRef cyber-aggression dataset deep learning designed detection developed distribution effective emotional Evaluation example experiments expressed extract F1 scores fear Figure final follows gender groups health insurance heatmap hybrid identify important improve input International labels language lexicon machine learning Marketing means measure method movie multi-label negative Neural Network nodes obtained performance personality positive prediction preferences present problem Proceedings product categories proposed psychographic recommendation representation represents scores segmentation selected semantic sentences sentiment analysis sentiment words shown shows similar structure Table task techniques tweets Twitter types understanding variables vector visualization word embedding word vectors