Sentiment analysis using a lexicon-based approach in Lisbon, Portugal
Advances in digital sensors and Information flow have created an abundance of data generated by users under various emotional states in different situations. Although this opens up a new facet in spatial research, the large amount of data makes it difficult to analyze and obtain complete and compre...
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| Format: | Article |
| Language: | English |
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PAGEPress Publications
2025-04-01
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| Series: | Geospatial Health |
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| Online Access: | https://www.geospatialhealth.net/gh/article/view/1344 |
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| author | Iuria Betco Ana Isabel Ribeiro David S. Vale Luis Encalada-Abarca Cláudia M. Viana Jorge Rocha |
| author_facet | Iuria Betco Ana Isabel Ribeiro David S. Vale Luis Encalada-Abarca Cláudia M. Viana Jorge Rocha |
| author_sort | Iuria Betco |
| collection | DOAJ |
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Advances in digital sensors and Information flow have created an abundance of data generated by users under various emotional states in different situations. Although this opens up a new facet in spatial research, the large amount of data makes it difficult to analyze and obtain complete and comprehensive information leading to an increase in the demand for sentiment analysis. In this study, the Canadian National Research Council (NRC) of Sentiment and Emotion Lexicon (EmoLex) was used, based on data from the social network Twitter (now X), thus enabling the identification of the places in Lisbon where both positive and negative sentiment prevails. From the results obtained, the Portuguese are happy in spaces associated with leisure and consumption, such as museums, event venues, gardens, shopping centres, stores, and restaurants. The high score of words associated with negative sentiment have more bias, since the lexicon sometimes has difficulties to identify the context in which the word appears, ending up giving it a negative score (e.g., war, terminal).
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| format | Article |
| id | doaj-art-cdddef1fceba412f88e7a3d1d55e0bf0 |
| institution | OA Journals |
| issn | 1827-1987 1970-7096 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | PAGEPress Publications |
| record_format | Article |
| series | Geospatial Health |
| spelling | doaj-art-cdddef1fceba412f88e7a3d1d55e0bf02025-08-20T02:19:07ZengPAGEPress PublicationsGeospatial Health1827-19871970-70962025-04-0120110.4081/gh.2025.1344Sentiment analysis using a lexicon-based approach in Lisbon, PortugalIuria Betco0https://orcid.org/0000-0001-8714-686XAna Isabel Ribeiro1https://orcid.org/0000-0001-8880-6962David S. Vale2https://orcid.org/0000-0002-1403-0628Luis Encalada-Abarca3Cláudia M. Viana4https://orcid.org/0000-0001-6858-4522Jorge Rocha5https://orcid.org/0000-0002-7228-6330Centre of Geographical Studies, Institute of Geography and Spatial Planning, University of LisbonEPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal; Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional (ITR), Porto, Portugal; Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina, Universidade do Porto, PortoCIAUD, Lisbon School of Architecture, University of LisbonCentre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, Portugal; Universidad Espíriitu Santo, Samborondón, Ecuador; Associate Laboratory Terra, LisbonCentre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, Portugal; EPIUnit - Instituto de Saúde Pública, Universidade do Porto, PortoCentre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, Portugal; Associate Laboratory Terra, Lisbon Advances in digital sensors and Information flow have created an abundance of data generated by users under various emotional states in different situations. Although this opens up a new facet in spatial research, the large amount of data makes it difficult to analyze and obtain complete and comprehensive information leading to an increase in the demand for sentiment analysis. In this study, the Canadian National Research Council (NRC) of Sentiment and Emotion Lexicon (EmoLex) was used, based on data from the social network Twitter (now X), thus enabling the identification of the places in Lisbon where both positive and negative sentiment prevails. From the results obtained, the Portuguese are happy in spaces associated with leisure and consumption, such as museums, event venues, gardens, shopping centres, stores, and restaurants. The high score of words associated with negative sentiment have more bias, since the lexicon sometimes has difficulties to identify the context in which the word appears, ending up giving it a negative score (e.g., war, terminal). https://www.geospatialhealth.net/gh/article/view/1344Sentiment analysislexicon approachtwitteremotionLisbon |
| spellingShingle | Iuria Betco Ana Isabel Ribeiro David S. Vale Luis Encalada-Abarca Cláudia M. Viana Jorge Rocha Sentiment analysis using a lexicon-based approach in Lisbon, Portugal Geospatial Health Sentiment analysis lexicon approach emotion Lisbon |
| title | Sentiment analysis using a lexicon-based approach in Lisbon, Portugal |
| title_full | Sentiment analysis using a lexicon-based approach in Lisbon, Portugal |
| title_fullStr | Sentiment analysis using a lexicon-based approach in Lisbon, Portugal |
| title_full_unstemmed | Sentiment analysis using a lexicon-based approach in Lisbon, Portugal |
| title_short | Sentiment analysis using a lexicon-based approach in Lisbon, Portugal |
| title_sort | sentiment analysis using a lexicon based approach in lisbon portugal |
| topic | Sentiment analysis lexicon approach emotion Lisbon |
| url | https://www.geospatialhealth.net/gh/article/view/1344 |
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