Sentiment Analysis for Social Media
by
Carlos A. Iglesias 1,*,†
and Antonio Moreno 2,†
Carlos A. Iglesias 1,*,† and Antonio Moreno 2,†
1
Intelligent Systems Group, ETSI Telecomunicación, Avda. Complutense 30, 28040 Madrid, Spain
2
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; https://doi.org/10.3390/app9235037
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
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Iglesias, C.A.; Moreno, A. Sentiment Analysis for Social Media. Appl. Sci. 2019, 9, 5037.
Show more citation formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
