A multi-label, semi-supervised classification approach applied to personality prediction in social media

Página do item simplificado
dc.contributor.authorLima A.C.E.S.
dc.contributor.authorde Castro L.N.
dc.date.accessioned2024-03-13T01:01:54Z
dc.date.available2024-03-13T01:01:54Z
dc.date.issued2014
dc.description.abstractSocial media allow web users to create and share content pertaining to different subjects, exposing their activities, opinions, feelings and thoughts. In this context, online social media has attracted the interest of data scientists seeking to understand behaviours and trends, whilst collecting statistics for social sites. One potential application for these data is personality prediction, which aims to understand a user's behaviour within social media. Traditional personality prediction relies on users' profiles, their status updates, the messages they post, etc. Here, a personality prediction system for social media data is introduced that differs from most approaches in the literature, in that it works with groups of texts, instead of single texts, and does not take users' profiles into account. Also, the proposed approach extracts meta-attributes from texts and does not work directly with the content of the messages. The set of possible personality traits is taken from the Big Five model and allows the problem to be characterised as a multi-label classification task. The problem is then transformed into a set of five binary classification problems and solved by means of a semi-supervised learning approach, due to the difficulty in annotating the massive amounts of data generated in social media. In our implementation, the proposed system was trained with three well-known machine-learning algorithms, namely a Naïve Bayes classifier, a Support Vector Machine, and a Multilayer Perceptron neural network. The system was applied to predict the personality of Tweets taken from three datasets available in the literature, and resulted in an approximately 83% accurate prediction, with some of the personality traits presenting better individual classification rates than others. © 2014 Elsevier Ltd.
dc.description.firstpage122
dc.description.lastpage130
dc.description.volume58
dc.identifier.doi10.1016/j.neunet.2014.05.020
dc.identifier.issn1879-2782
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/36461
dc.relation.ispartofNeural Networks
dc.rightsAcesso Restrito
dc.subject.otherlanguageBig Five
dc.subject.otherlanguageMulti-label classification
dc.subject.otherlanguagePersonality
dc.subject.otherlanguageSemi-supervised learning
dc.subject.otherlanguageSocial media
dc.subject.otherlanguageTwitter
dc.titleA multi-label, semi-supervised classification approach applied to personality prediction in social media
dc.typeArtigo
local.scopus.citations89
local.scopus.eid2-s2.0-84906075994
local.scopus.subjectBig five
local.scopus.subjectMulti-label classifications
local.scopus.subjectPersonality
local.scopus.subjectSemi-supervised learning
local.scopus.subjectSocial media
local.scopus.subjectTwitter
local.scopus.subjectAlgorithms
local.scopus.subjectArtificial Intelligence
local.scopus.subjectBayes Theorem
local.scopus.subjectHumans
local.scopus.subjectNeural Networks (Computer)
local.scopus.subjectPersonality
local.scopus.subjectSocial Media
local.scopus.subjectSupport Vector Machines
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84906075994&origin=inward
Arquivos
Coleções
Artigos de periódico