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210512 ||| eng |
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|a 9783039285730
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|a 9783039285723
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|a books978-3-03928-573-0
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|a Moreno, Antonio
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| 245 |
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|a Sentiment Analysis for Social Media
|h Elektronische Ressource
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| 260 |
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|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2020
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| 300 |
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|a 1 electronic resource (152 p.)
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| 653 |
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|a machine learning
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| 653 |
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|a semantic networks
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| 653 |
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|a psychographic segmentation
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| 653 |
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|a violence against women
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| 653 |
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|a random forest
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| 653 |
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|a big data-driven marketing
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| 653 |
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|a emotion classification
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| 653 |
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|a online review
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| 653 |
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|a deep learning
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| 653 |
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|a History of engineering and technology / bicssc
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| 653 |
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|a social media
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| 653 |
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|a collaborative schemes of sentiment analysis and sentiment systems
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| 653 |
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|a affect computing
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| 653 |
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|a user preference prediction
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| 653 |
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|a provider networks
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| 653 |
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|a racism
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| 653 |
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|a text mining
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| 653 |
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|a review data mining
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| 653 |
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|a sentiment word analysis
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| 653 |
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|a health insurance
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| 653 |
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|a word association
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| 653 |
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|a social networks
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| 653 |
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|a sentiment lexicon
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| 653 |
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|a sentiment analysis
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| 653 |
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|a violence based on sexual orientation
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| 653 |
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|a text feature representation
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| 653 |
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|a cyber-aggression
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| 653 |
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|a medical web forum
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| 653 |
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|a Twitter
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| 653 |
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|a gender classification
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| 653 |
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|a lexicon construction
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| 653 |
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|a opinion mining
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| 653 |
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|a sentiment classification
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| 653 |
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|a hybrid vectorization
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| 653 |
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|a convolutional neural network
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| 653 |
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|a emotion analysis
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| 653 |
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|a recommender system
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| 653 |
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|a sentiment-aware word embedding
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| 700 |
1 |
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|a Iglesias, Carlos A.
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
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| 028 |
5 |
0 |
|a 10.3390/books978-3-03928-573-0
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| 856 |
4 |
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|u https://www.mdpi.com/books/pdfview/book/2154
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/59238
|z DOAB: description of the publication
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|a 900
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|a 610
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|a 658
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|a 620
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|a 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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