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An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data

Sentiment Analysis for Social Media

by 1,*,† and 2,†
Intelligent Systems Group, ETSI Telecomunicación, Avda. Complutense 30, 28040 Madrid, Spain
Intelligent Technologies for Advance Knowledge Acquisition (ITAKA) Group, Escola Tècnica Superior d’Enginyeria, Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2019, 9(23), 5037;
Received: 31 October 2019 / Accepted: 19 November 2019 / Published: 22 November 2019
(This article belongs to the Special Issue Sentiment Analysis for Social Media)
Sentiment analysis has become a key technology to gain insight from social networks. The field has reached a level of maturity that paves the way for its exploitation in many different fields such as marketing, health, banking or politics. The latest technological advancements, such as deep learning techniques, have solved some of the traditional challenges in the area caused by the scarcity of lexical resources. In this Special Issue, different approaches that advance this discipline are presented. The contributed articles belong to two broad groups: technological contributions and applications. View Full-Text
Keywords: sentiment analysis; emotion analysis; social media; affect computing sentiment analysis; emotion analysis; social media; affect computing
MDPI and ACS Style

Iglesias, C.A.; Moreno, A. Sentiment Analysis for Social Media. Appl. Sci. 2019, 9, 5037.

AMA Style

Iglesias CA, Moreno A. Sentiment Analysis for Social Media. Applied Sciences. 2019; 9(23):5037.

Chicago/Turabian Style

Iglesias, Carlos A., and Antonio Moreno. 2019. "Sentiment Analysis for Social Media" Applied Sciences 9, no. 23: 5037.

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