Detecting psychological sentiments in users from social networks
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/21823 |
Resumo: | Over the years there has been an increase in the population suffering from depression. Many users find refuge in social networks as an alternative to health professionals, because they feel safer and comfortable to open up with others without forcing them to express their opinion. Despite the negative impact of social networks on users, many studies show that based on the posts made, it is possible to make a distinction between depressed and non-depressed users and a behavioural and linguistic analysis. In this way, the literature reports that prediction models of depressive users have been created and improved. In this dissertation, we aim to test and develop a model that helps in the detection of depressive users in social networks based on the words and emotions of the posts, using two lexicons, NRC Emotion Lexicon and VAD Lexicon. Through the NRC Emotion Lexicon we wanted to evaluate if the implementation of new features in a study presented in the related work, allows an improvement in the performance of the model developed. With VAD Lexicon, the objective is to analyze if through VAD values it is possible to extract the emotions. We concluded that the VAD Lexicon was not advantageous since, it was not possible to correlate the VAD values with the emotions. With NRC Emotion Lexicon, we concluded that although the integration of the lexicon did not result in a better performance, it was possible to observe improvements, demonstrating that the lexicon contributed positively to the effectiveness of the model. |
id |
RCAP_f2cbaba742cfc686145062693e7d3f0d |
---|---|
oai_identifier_str |
oai:repositorio.iscte-iul.pt:10071/21823 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Detecting psychological sentiments in users from social networksSentiment analysisSocial networksTwitterRedditMental healthDepressionAnálise de sentimentosRede socialSaúde mentalDepressãoOver the years there has been an increase in the population suffering from depression. Many users find refuge in social networks as an alternative to health professionals, because they feel safer and comfortable to open up with others without forcing them to express their opinion. Despite the negative impact of social networks on users, many studies show that based on the posts made, it is possible to make a distinction between depressed and non-depressed users and a behavioural and linguistic analysis. In this way, the literature reports that prediction models of depressive users have been created and improved. In this dissertation, we aim to test and develop a model that helps in the detection of depressive users in social networks based on the words and emotions of the posts, using two lexicons, NRC Emotion Lexicon and VAD Lexicon. Through the NRC Emotion Lexicon we wanted to evaluate if the implementation of new features in a study presented in the related work, allows an improvement in the performance of the model developed. With VAD Lexicon, the objective is to analyze if through VAD values it is possible to extract the emotions. We concluded that the VAD Lexicon was not advantageous since, it was not possible to correlate the VAD values with the emotions. With NRC Emotion Lexicon, we concluded that although the integration of the lexicon did not result in a better performance, it was possible to observe improvements, demonstrating that the lexicon contributed positively to the effectiveness of the model.Ao longo dos anos tem havido um aumento da população que sofre de depressão. Muitos utilizadores encontram um refúgio nas redes sociais em alternativa a profissionais de saúde, por se sentirem mais seguros e confortáveis a desabafar com outros utilizadores sem que isso os obrigue a expressar uma opinião. Apesar do impacto negativo que as redes sociais podem ter nos utilizadores, muitos estudos mostram que com base nos posts feitos, é possível fazer uma distinção entre utilizadores depressivos e não depressivos e uma análise comportamental e linguística. Desta maneira, a literatura reporta que foram criados e aperfeiçoados modelos de predição de utilizadores depressivos. Nesta dissertação, temos como objetivo testar e desenvolver um modelo que ajude na deteção de utilizadores depressivos em redes sociais com base nas palavras e emoções do posts, utilizando dois léxicos, NRC Emotion Lexicon e VAD Lexicon. Através do NRC Emotion Lexicon quisemos avaliar se a implementação de novas features num estudo apresentado no trabalho relacionado, permite uma melhoria no desempenho do modelo desenvolvido. Já com o VAD Lexicon, o objetivo é analisar se através dos valores obtidos de VAD é possível extrair as emoções presentes. Concluímos que o uso do VAD Lexicon não foi vantajoso visto que, não foi possível correlacionar os valores do VAD com os sentimentos. Para o NRC Emotion Lexicon, concluímos que, apesar da integração do léxico no modelo não ter resultado numa melhor performance do modelo, foi possível observar melhorias, demonstrando que o léxico contribuiu positivamente para a eficácia do modelo.2021-02-02T18:49:45Z2020-11-24T00:00:00Z2020-11-242020-10info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10071/21823TID:202578631engSantos, Patrícia de Sousa dosinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:56:26Zoai:repositorio.iscte-iul.pt:10071/21823Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:28:56.073147Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Detecting psychological sentiments in users from social networks |
title |
Detecting psychological sentiments in users from social networks |
spellingShingle |
Detecting psychological sentiments in users from social networks Santos, Patrícia de Sousa dos Sentiment analysis Social networks Mental health Depression Análise de sentimentos Rede social Saúde mental Depressão |
title_short |
Detecting psychological sentiments in users from social networks |
title_full |
Detecting psychological sentiments in users from social networks |
title_fullStr |
Detecting psychological sentiments in users from social networks |
title_full_unstemmed |
Detecting psychological sentiments in users from social networks |
title_sort |
Detecting psychological sentiments in users from social networks |
author |
Santos, Patrícia de Sousa dos |
author_facet |
Santos, Patrícia de Sousa dos |
author_role |
author |
dc.contributor.author.fl_str_mv |
Santos, Patrícia de Sousa dos |
dc.subject.por.fl_str_mv |
Sentiment analysis Social networks Mental health Depression Análise de sentimentos Rede social Saúde mental Depressão |
topic |
Sentiment analysis Social networks Mental health Depression Análise de sentimentos Rede social Saúde mental Depressão |
description |
Over the years there has been an increase in the population suffering from depression. Many users find refuge in social networks as an alternative to health professionals, because they feel safer and comfortable to open up with others without forcing them to express their opinion. Despite the negative impact of social networks on users, many studies show that based on the posts made, it is possible to make a distinction between depressed and non-depressed users and a behavioural and linguistic analysis. In this way, the literature reports that prediction models of depressive users have been created and improved. In this dissertation, we aim to test and develop a model that helps in the detection of depressive users in social networks based on the words and emotions of the posts, using two lexicons, NRC Emotion Lexicon and VAD Lexicon. Through the NRC Emotion Lexicon we wanted to evaluate if the implementation of new features in a study presented in the related work, allows an improvement in the performance of the model developed. With VAD Lexicon, the objective is to analyze if through VAD values it is possible to extract the emotions. We concluded that the VAD Lexicon was not advantageous since, it was not possible to correlate the VAD values with the emotions. With NRC Emotion Lexicon, we concluded that although the integration of the lexicon did not result in a better performance, it was possible to observe improvements, demonstrating that the lexicon contributed positively to the effectiveness of the model. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-11-24T00:00:00Z 2020-11-24 2020-10 2021-02-02T18:49:45Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/21823 TID:202578631 |
url |
http://hdl.handle.net/10071/21823 |
identifier_str_mv |
TID:202578631 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799134851984523264 |