Evaluating Citizens’ Sentiments in Smart Cities: A Deep Learning Approach
Sentiment analysis of user-generated online content is crucial for smart city analytics and relevant social services. Researchers have relied mainly on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users a...
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2021
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| Online Access: | https://bspace.buid.ac.ae/handle/1234/3072 https://doi.org/10.23919/SpliTech49282.2020.9243768. |
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| _version_ | 1862980613949095936 |
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| author | Elabora, Abdallah |
| author2 | Alkhatib, Manar Samuel Mathew, Sujith El Barachi, May |
| author2_role | author author author |
| author_facet | Elabora, Abdallah Alkhatib, Manar Samuel Mathew, Sujith El Barachi, May |
| author_role | author |
| dc.creator.none.fl_str_mv | Elabora, Abdallah Alkhatib, Manar Samuel Mathew, Sujith El Barachi, May |
| dc.date.none.fl_str_mv | 2021-08-17 2025-05-15T11:12:31Z 2025-05-15T11:12:31Z |
| dc.identifier.none.fl_str_mv | Elabora, A. et al. (2020) “Evaluating Citizens’ Sentiments in Smart Cities: A Deep Learning Approach,” in 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech), pp. 1–5. https://bspace.buid.ac.ae/handle/1234/3072 https://doi.org/10.23919/SpliTech49282.2020.9243768. |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech)1-5 |
| dc.subject.none.fl_str_mv | Sentiment analysis, Face Recognition, Convolutional Neural Network, Deep Learning |
| dc.title.none.fl_str_mv | Evaluating Citizens’ Sentiments in Smart Cities: A Deep Learning Approach |
| dc.type.none.fl_str_mv | Article |
| description | Sentiment analysis of user-generated online content is crucial for smart city analytics and relevant social services. Researchers have relied mainly on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their feelings and share emotions. Sentiment analysis of such large scale visual content, such as those in image tweets, helps to obtain user sentiments toward events or topics and therefore complement textual sentiment analysis. Motivated by the need to leverage large scale yet noisy training data to solve the extremely challenging problem of face sentiment analysis, we employ Convolutional Neural Networks (CNN). We designed a suitable CNN architecture to classify facial emotions and analyze sentiments. We have conducted extensive experiments on labeled images. The results show that the proposed CNN achieved a very good performance in face sentiment analysis with 89.9% of F1-measure |
| id | budr_cac67a3fefc83a825787b372cdd50c05 |
| identifier_str_mv | Elabora, A. et al. (2020) “Evaluating Citizens’ Sentiments in Smart Cities: A Deep Learning Approach,” in 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech), pp. 1–5. |
| language_invalid_str_mv | en_US |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/3072 |
| publishDate | 2021 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Evaluating Citizens’ Sentiments in Smart Cities: A Deep Learning ApproachElabora, AbdallahAlkhatib, ManarSamuel Mathew, SujithEl Barachi, MaySentiment analysis, Face Recognition, Convolutional Neural Network, Deep LearningSentiment analysis of user-generated online content is crucial for smart city analytics and relevant social services. Researchers have relied mainly on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their feelings and share emotions. Sentiment analysis of such large scale visual content, such as those in image tweets, helps to obtain user sentiments toward events or topics and therefore complement textual sentiment analysis. Motivated by the need to leverage large scale yet noisy training data to solve the extremely challenging problem of face sentiment analysis, we employ Convolutional Neural Networks (CNN). We designed a suitable CNN architecture to classify facial emotions and analyze sentiments. We have conducted extensive experiments on labeled images. The results show that the proposed CNN achieved a very good performance in face sentiment analysis with 89.9% of F1-measureIEEE2025-05-15T11:12:31Z2025-05-15T11:12:31Z2021-08-17ArticleElabora, A. et al. (2020) “Evaluating Citizens’ Sentiments in Smart Cities: A Deep Learning Approach,” in 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech), pp. 1–5.https://bspace.buid.ac.ae/handle/1234/3072https://doi.org/10.23919/SpliTech49282.2020.9243768.en_US2020 5th International Conference on Smart and Sustainable Technologies (SpliTech)1-5oai:bspace.buid.ac.ae:1234/30722026-01-29T15:40:11Z |
| spellingShingle | Evaluating Citizens’ Sentiments in Smart Cities: A Deep Learning Approach Elabora, Abdallah Sentiment analysis, Face Recognition, Convolutional Neural Network, Deep Learning |
| title | Evaluating Citizens’ Sentiments in Smart Cities: A Deep Learning Approach |
| title_full | Evaluating Citizens’ Sentiments in Smart Cities: A Deep Learning Approach |
| title_fullStr | Evaluating Citizens’ Sentiments in Smart Cities: A Deep Learning Approach |
| title_full_unstemmed | Evaluating Citizens’ Sentiments in Smart Cities: A Deep Learning Approach |
| title_short | Evaluating Citizens’ Sentiments in Smart Cities: A Deep Learning Approach |
| title_sort | Evaluating Citizens’ Sentiments in Smart Cities: A Deep Learning Approach |
| topic | Sentiment analysis, Face Recognition, Convolutional Neural Network, Deep Learning |
| url | https://bspace.buid.ac.ae/handle/1234/3072 https://doi.org/10.23919/SpliTech49282.2020.9243768. |