Sentiment Analysis for Social Media

Sentiment Analysis for Social Media

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 indus...

Full description

Saved in:
Bibliographic Details
Main Author: Moreno, Antonio (auth)
Other Authors: Iglesias, Carlos A. (auth)
Format: Book Chapter
Published: MDPI - Multidisciplinary Digital Publishing Institute 2020
Subjects:
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
Online Access:Get Fullteks
DOAB: description of the publication
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Physical Description:1 electronic resource (152 p.)
ISBN:books978-3-03928-573-0
9783039285730
9783039285723
Access:Open Access

Similar Items