dc.creator | Barba González, Cristóbal | es |
dc.creator | García Nieto, José Manuel | es |
dc.creator | Navas Delgado, Ismael | es |
dc.creator | Aldana Montes, José F. | es |
dc.date.accessioned | 2021-05-03T11:52:33Z | |
dc.date.available | 2021-05-03T11:52:33Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | Barba González, C., García Nieto, J.M., Navas Delgado, I. y Aldana Montes, J.F. (2016). A Fine Grain Sentiment Analysis with Semantics in Tweets. International Journal of Interactive Multimedia and Artificial Intelligence, 3 (6), 22-28. | |
dc.identifier.issn | 1989-1660 | es |
dc.identifier.uri | https://hdl.handle.net/11441/108366 | |
dc.description.abstract | Social networking is nowadays a major source of
new information in the world. Microblogging sites like Twitter
have millions of active users (320 million active users on Twitter
on the 30th September 2015) who share their opinions in real
time, generating huge amounts of data. These data are, in most
cases, available to any network user. The opinions of Twitter users
have become something that companies and other organisations
study to see whether or not their users like the products or services
they offer. One way to assess opinions on Twitter is classifying
the sentiment of the tweets as positive or negative. However, this
process is usually done at a coarse grain level and the tweets are
classified as positive or negative. However, tweets can be partially
positive and negative at the same time, referring to different
entities. As a result, general approaches usually classify these
tweets as “neutral”. In this paper, we propose a semantic analysis
of tweets, using Natural Language Processing to classify the
sentiment with regards to the entities mentioned in each tweet. We
offer a combination of Big Data tools (under the Apache Hadoop
framework) and sentiment analysis using RDF graphs supporting
the study of the tweet’s lexicon. This work has been empirically
validated using a sporting event, the 2014 Phillips 66 Big 12 Men’s
Basketball Championship. The experimental results show a clear
correlation between the predicted sentiments with specific events
during the championship. | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación TIN2014-58304-R | es |
dc.description.sponsorship | Junta de Andalucía P11-TIC-7529 | es |
dc.description.sponsorship | Junta de Andalucía P12- TIC-1519 | es |
dc.format | application/pdf | es |
dc.format.extent | 7 | es |
dc.language.iso | eng | es |
dc.publisher | Universidad Internacional de La Rioja (UNIR) | es |
dc.relation.ispartof | International Journal of Interactive Multimedia and Artificial Intelligence, 3 (6), 22-28. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Microblogging | es |
dc.subject | Big Data | es |
dc.subject | Sentiment analysis | es |
dc.subject | Apache Hadoop | es |
dc.subject | MapReduce | es |
dc.subject | Twitter | es |
dc.subject | RDF | es |
dc.subject | Named-entity recognition | es |
dc.subject | Linked data | es |
dc.title | A Fine Grain Sentiment Analysis with Semantics in Tweets | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.projectID | TIN2014-58304-R | es |
dc.relation.projectID | P11-TIC-7529 | es |
dc.relation.projectID | P12- TIC-1519 | es |
dc.relation.publisherversion | https://www.ijimai.org/journal/bibcite/reference/2533 | es |
dc.identifier.doi | 10.9781/ijimai.2016.363 | es |
dc.journaltitle | International Journal of Interactive Multimedia and Artificial Intelligence | es |
dc.publication.volumen | 3 | es |
dc.publication.issue | 6 | es |
dc.publication.initialPage | 22 | es |
dc.publication.endPage | 28 | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (MICIN). España | es |
dc.contributor.funder | Junta de Andalucía | es |