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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 --- sentiment analysis --- emotion classification --- deep learning --- Twitter --- sentiment analysis --- gender classification --- machine learning --- deep learning --- medical web forum --- emotion classification --- sentiment lexicon --- text feature representation --- hybrid vectorization --- sentiment-aware word embedding --- cyber-aggression --- sentiment analysis --- random forest --- racism --- violence based on sexual orientation --- violence against women --- social networks --- psychographic segmentation --- user preference prediction --- lexicon construction --- online review --- recommender system --- big data-driven marketing --- social media --- Twitter --- text mining --- sentiment analysis --- word association --- health insurance --- provider networks --- deep learning --- convolutional neural network --- sentiment classification --- collaborative schemes of sentiment analysis and sentiment systems --- review data mining --- semantic networks --- sentiment word analysis --- sentiment analysis --- emotion analysis --- social media --- affect computing
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The book features contributions that report original research in the theoretical, technological, and social aspects of geoinformation methods, as applied to supporting citizen science. Specifically, the book focuses on the technological aspects of the field and their application toward the recruitment of volunteers and the collection, management, and analysis of geotagged information to support volunteer involvement in scientific projects. Internationally renowned research groups share research in three areas: First, the key methods of geoinformatics within citizen science initiatives to support scientists in discovering new knowledge in specific application domains or in performing relevant activities, such as reliable geodata filtering, management, analysis, synthesis, sharing, and visualization; second, the critical aspects of citizen science initiatives that call for emerging or novel approaches of geoinformatics to acquire and handle geoinformation; and third, novel geoinformatics research that could serve in support of citizen science.
volunteer geographic information --- positional accuracy --- land administration systems --- location-based social networks (LBSNs) --- clustering --- user preference --- social relationship effect --- spatial proximity --- crowdsourcing --- volunteered geographic information (VGI) --- ensemble --- classification accuracy --- latent class analysis --- OpenStreetMap --- VGI --- community mapping --- data analysis --- GIS education --- data import --- citizen science --- marine mammal --- opportunistic data --- Alaska --- spatial bias --- sample size --- volunteer --- education --- recruitment --- Pentatomidae --- Environmental niche modeling --- citizen science --- crowdsourcing --- MaxEnt --- QGIS --- brown marmorated stink bug --- air quality estimation --- air pollution --- citizen science --- sky images --- social media --- data fusion --- citizen science --- volunteered geographic information (VGI) --- toponym --- crowdsourced data collection --- data quality --- GIS --- digital cartography --- algorithms --- spatial accuracy --- analysis --- OpenStreetMap --- citizen science --- geoinformatics --- projects survey --- geoinformation in citizen science --- VGI in citizen science --- crowdsourced geoinformation collection and analysis
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