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Sentiment Analysis for Social Media



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Autore: Moreno Antonio Visualizza persona
Titolo: Sentiment Analysis for Social Media Visualizza cluster
Pubblicazione: MDPI - Multidisciplinary Digital Publishing Institute, 2020
Descrizione fisica: 1 electronic resource (152 p.)
Soggetto non controllato: 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
Persona (resp. second.): IglesiasCarlos A
Sommario/riassunto: 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.
Titolo autorizzato: Sentiment Analysis for Social Media  Visualizza cluster
ISBN: 3-03928-573-4
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910404092303321
Lo trovi qui: Univ. Federico II
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