Cited By
View all- Cortis KDavis B(2021)Over a decade of social opinion mining: a systematic reviewArtificial Intelligence Review10.1007/s10462-021-10030-254:7(4873-4965)Online publication date: 1-Oct-2021
Towards the study of sentiment in the public opinion of science in Spanish
Pages 963 - 970
Abstract
Every day millions of short messages that show opinions, information and contents of all kinds move around the networks. The analysis of this large volume of data is possible thanks to computer techniques. The sentiments of the messages can provide observations on the acceptance of topics, social trends or currents of opinion. Therefore, this research is part of a project that addresses the creation of a prototype for the analysis of the sentiment of messages on scientific topics on Twitter using supervised machine learning algorithms. These methods require having a large set of data labeled (corpus), to train the model in the best possible way. The detailed process of creating this corpus is the objective of this dissertation. The ultimate goal of the project is to create a function that is able to predict what the value of an input element would be after having been trained with the sentiment classifier. The first results of the classifier show a reliability around 70% in the tested algorithms and from them you can extract adjusted classifications in real time connected to the Twitter Streaming API.
References
[1]
A. Java, X. Song, T. Finin, and B. Tseng, "Why We Twitter: Understanding Microblogging Usage and Communities," in 9th WEBKDD and 1st SNA-KDD Workshop, 2007.
[2]
S. G. Esparza, M. P. O 'mahony, and B. Smyth, "Mining the real-time web: A novel approach to product recommendation," Knowledge-Based Syst., vol. 29, pp. 3--11, 2012.
[3]
H. Kwak, C. Lee, H. Park, and S. Moon, "What is Twitter, a social network or a news media?," in Proceedings of the 19th International conference on World wide web - WWW '10, 2010, p. 591.
[4]
K. Raes, "La responsabilidad social de los científicos," IPTS Rep., vol. 72, 2003.
[5]
M. Baker, "Social media: A network boost," Nature, vol. 518, no. 7538, pp.263--265, Feb. 2015.
[6]
J. Segarra-Saavedra, V. Tur-Viñes, and T. Hidalgo-Marí, "Uso de Twitter como herramienta de difusión en las revistas científicas españolas de Comunicación," in 7a Conferencia International sobre revistas de ciencias sociales y humanidades., 2017.
[7]
C. Pont-Sorribes, S. Cortiñas-Rovira, and I. Di Bonito, "Challenges and opportunities for science journalists in adopting new technologies: the case of Spain," SISSA-International Sch. Adv. Stud., 2013.
[8]
H. P. Peters, S. Dunwoody, J. Allgaier, Y.-Y. Lo, and D. Brossard, "Public communication of science 2.0: Is the communication of science via the "new media" online a genuine transformation or old wine in new bottles?," EMBO Rep., vol. 15, no. 7, pp. 749--53, Jul. 2014.
[9]
C. Ribas, "La divulgación y la comunicación de la ciencia, en la encrucijada," SEBBM, no. 173, pp. 10--12, 2012.
[10]
P. B. Jarreau, "All the Science That Is Fit to Blog: An Analysis of Science Blogging Practices," no. May 2010, 2015.
[11]
A. Pérez-Rodríguez, C. González-Pedraz, and J. Alonso, "Twitter como herramienta de comunicación científica en España. Principales agentes y redes de comunicación," vol. 7, pp. 95--111, 2018.
[12]
M. Á. García Cumbreras, E. M. Cámara, J. V. Román, and J. G. Morera, "TASS 2015 - The evolution of the Spanish opinion mining systems," Proces. Leng. Nat., vol. 56, pp. 33--40, 2016.
[13]
L.-F. Hurtado, F. Pla, and D. Buscaldi, "ELiRF-UPV en TASS 2015: Análisis de Sentimientos en Twitter," in Workshop on Sentiment Analysis at SEPLN co-located with 31st SEPLN Conference (SEPLN 2015), 2015, pp. 75--79.
[14]
C. Arcila-Calderón, E. Barbosa-Caro, and F. Cabezuelo-Lorenzo, "Técnicas Big Data: Análisis de textos a gran escala para la investigación científica y periodística," El Prof. la Inf., vol. 25, no. 4, pp. 623--631, 2016.
[15]
C. Arcila-Calderón, F. Ortega-Mohedano, J. Jiménez-Amores, and S. Trullenque, "Análisis supervisado de sentimientos políticos en español: clasificación en tiempo real de tweets basada en aprendizaje automático," vol. 26, no. 5, pp. 1699--2407, 2017.
[16]
J. Hernández Orallo, M. J. Ramírez Quintana, and C. Ferri Ramínez, Introducción a la Minería de Datos. Pearson Educación, 2004.
[17]
B. Pang and L. Lee, "Opinion mining and sentiment analysis," 2008.
[18]
F. Bravo-Marquez, M. Mendoza, and B. Poblete, "Meta-level sentiment models for big social data analysis," Knowledge-Based Syst., vol. 69, pp. 86--99, Oct. 2014.
[19]
T. Baviera, "Técnicas para el análisis del sentimiento en Twitter: Aprendizaje Automático Supervisado y SentiStrength Tecnhiques for sentiment analysis in Twitter: Supervised Learning and SentiStrength," pp. 33--50, 2016.
[20]
W. Medhat, A. Hassan, and H. Korashy, "Sentiment analysis algorithms and applications: A survey," Ain Shams Eng. J., vol. 5, pp. 1093--1113, 2014.
[21]
S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python. O'Reilly, 2009.
[22]
L. Dubiau and J. M. Ale, "Análisis de Sentimientos sobre un Corpus en Español: Experimentación con un Caso de Estudio," in 14th Argentine Symposium on Artificial Intelligence, ASAI 2013, 2013, pp. 36--47.
[23]
F. J. García-Peñalvo, "Formación en la sociedad del conocimiento, un programa de doctorado con una perspectiva interdisciplinar," vol. 15, no. 1, pp. 4--9, 2013.
[24]
F. J. García-Peñalvo, "Education in knowledge society: a new PhD programme approach," in Proceedings of the First International Conference on Technological Ecosystem for Enhancing Multiculturality (TEEM '13), 2013, pp. 575--577.
Index Terms
- Towards the study of sentiment in the public opinion of science in Spanish
Recommendations
Twitter Opinion Topic Model: Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge ManagementAspect-based opinion mining is widely applied to review data to aggregate or summarize opinions of a product, and the current state-of-the-art is achieved with Latent Dirichlet Allocation (LDA)-based model. Although social media data like tweets are ...
Social sentiment sensor: a visualization system for topic detection and topic sentiment analysis on microblog
As a new form of social media, microblogging provides platform sharing, wherein users can share their feelings and ideas on certain topics. Bursty topics from microblogs are the results of the emerging issues that instantly attract more followers and ...
Entity-centric topic-oriented opinion summarization in twitter
KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data miningMicroblogging services, such as Twitter, have become popular channels for people to express their opinions towards a broad range of topics. Twitter generates a huge volume of instant messages (i.e. tweets) carrying users' sentiments and attitudes every ...
Comments
Information & Contributors
Information
Published In
Copyright © 2018 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- University of Salamanca: University of Salamanca
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 October 2018
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
TEEM'18
TEEM'18: Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality
October 24 - 26, 2018
Salamanca, Spain
Acceptance Rates
TEEM'18 Paper Acceptance Rate 151 of 243 submissions, 62%;
Overall Acceptance Rate 496 of 705 submissions, 70%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 553Total Downloads
- Downloads (Last 12 months)4
- Downloads (Last 6 weeks)0
Reflects downloads up to 26 Jul 2024
Other Metrics
Citations
Cited By
View all- Cortis KDavis B(2021)Over a decade of social opinion mining: a systematic reviewArtificial Intelligence Review10.1007/s10462-021-10030-254:7(4873-4965)Online publication date: 1-Oct-2021
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in