A novel sentiment analysis framework for monitoring the evolving public opinion in real-time: Case study on climate change
Smart city analytics involves tracking, interpreting, and evaluating the sentiments and emotions that are shared via online social media channels. Sentiment analysis of social media posts has become increasingly prominent in recent years as a means of gaining insights into how members of the public...
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2021
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| Online Access: | https://bspace.buid.ac.ae/handle/1234/3050 https://doi.org/10.1016/j.jclepro.2021.127820. |
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| _version_ | 1862980613095555072 |
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| author | El Barachi, May |
| author2 | Alkhatib, Manar Mathew, Sujith Oroumchian, Farhad |
| author2_role | author author author |
| author_facet | El Barachi, May Alkhatib, Manar Mathew, Sujith Oroumchian, Farhad |
| author_role | author |
| dc.creator.none.fl_str_mv | El Barachi, May Alkhatib, Manar Mathew, Sujith Oroumchian, Farhad |
| dc.date.none.fl_str_mv | 2021-06-06 2025-05-15T10:05:38Z 2025-05-15T10:05:38Z |
| dc.identifier.none.fl_str_mv | El Barachi, M. et al. (2021) “A novel sentiment analysis framework for monitoring the evolving public opinion in real-time: Case study on climate change,” Journal of Cleaner Production, 312. 0959-6526 0959-6526 https://bspace.buid.ac.ae/handle/1234/3050 https://doi.org/10.1016/j.jclepro.2021.127820. |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.relation.none.fl_str_mv | Journal of Cleaner Productionv312 (20210820) |
| dc.subject.none.fl_str_mv | Smart cities Sentiment analysis Opinion leaders Social media analytics Climate change |
| dc.title.none.fl_str_mv | A novel sentiment analysis framework for monitoring the evolving public opinion in real-time: Case study on climate change |
| dc.type.none.fl_str_mv | Article |
| description | Smart city analytics involves tracking, interpreting, and evaluating the sentiments and emotions that are shared via online social media channels. Sentiment analysis of social media posts has become increasingly prominent in recent years as a means of gaining insights into how members of the public perceive current affairs. The ongoing research in this domain has leveraged multiple types of sentiment analysis. However, although the existing approaches enable researchers to acquire retrospective insights into public opinion, they do not enable a real- time evaluation. In addition, they are not readily scalable and necessitate the analysis of a significant amount of posts (in the millions) to facilitate a more in-depth evaluation. The study outlined in this paper was designed to address these shortcomings by presenting a framework that facilitates a real-time evaluation of the evolution of opinions shared by prominent public features and their respective followers; that is, high-impact posts. The developed solution encompasses a sophisticated Bi-directional LSTM classifier that was implemented and tested using a dataset consisting of 278,000 tweets related to the topic of climate change. The outcomes reveal that the proposed classifier achieved the following accuracies: 88.41% for discrimination; 89.66% for anger; 87.01% for inspiration; and 87.52% for joy - with negative emotions being more accurately classified than positive emotions. Similarly, the sentiment classification performance yielded accuracies of 89.32% for support and 89.80% for strong support, as well as 88.14% for opposition and 87.52% for strong opposition. In addition, the findings of the study indicated that geographic location, chosen topic, cultural sensitivities, and posting frequency all play a critical role in public reactions to posts and the ensuing perspectives they adopt. The solution stands out from existing retrospective analysis methods because it does not rely on the analysis of vast quantities of data records; rather, it can perform real-time, high-impact content analysis in a resource-efficient and sustainable manner. This framework can be used to generate insights into how public opinion is developing on a real-time basis. As such, it can have meaningful application within social media analysis efforts. |
| id | budr_c8c18451c17f47499b31bf8304b60ab2 |
| identifier_str_mv | El Barachi, M. et al. (2021) “A novel sentiment analysis framework for monitoring the evolving public opinion in real-time: Case study on climate change,” Journal of Cleaner Production, 312. 0959-6526 |
| 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:1234/3050 |
| publishDate | 2021 |
| publisher.none.fl_str_mv | Elsevier |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | A novel sentiment analysis framework for monitoring the evolving public opinion in real-time: Case study on climate changeEl Barachi, MayAlkhatib, ManarMathew, SujithOroumchian, FarhadSmart cities Sentiment analysis Opinion leaders Social media analytics Climate changeSmart city analytics involves tracking, interpreting, and evaluating the sentiments and emotions that are shared via online social media channels. Sentiment analysis of social media posts has become increasingly prominent in recent years as a means of gaining insights into how members of the public perceive current affairs. The ongoing research in this domain has leveraged multiple types of sentiment analysis. However, although the existing approaches enable researchers to acquire retrospective insights into public opinion, they do not enable a real- time evaluation. In addition, they are not readily scalable and necessitate the analysis of a significant amount of posts (in the millions) to facilitate a more in-depth evaluation. The study outlined in this paper was designed to address these shortcomings by presenting a framework that facilitates a real-time evaluation of the evolution of opinions shared by prominent public features and their respective followers; that is, high-impact posts. The developed solution encompasses a sophisticated Bi-directional LSTM classifier that was implemented and tested using a dataset consisting of 278,000 tweets related to the topic of climate change. The outcomes reveal that the proposed classifier achieved the following accuracies: 88.41% for discrimination; 89.66% for anger; 87.01% for inspiration; and 87.52% for joy - with negative emotions being more accurately classified than positive emotions. Similarly, the sentiment classification performance yielded accuracies of 89.32% for support and 89.80% for strong support, as well as 88.14% for opposition and 87.52% for strong opposition. In addition, the findings of the study indicated that geographic location, chosen topic, cultural sensitivities, and posting frequency all play a critical role in public reactions to posts and the ensuing perspectives they adopt. The solution stands out from existing retrospective analysis methods because it does not rely on the analysis of vast quantities of data records; rather, it can perform real-time, high-impact content analysis in a resource-efficient and sustainable manner. This framework can be used to generate insights into how public opinion is developing on a real-time basis. As such, it can have meaningful application within social media analysis efforts.Elsevier2025-05-15T10:05:38Z2025-05-15T10:05:38Z2021-06-06ArticleEl Barachi, M. et al. (2021) “A novel sentiment analysis framework for monitoring the evolving public opinion in real-time: Case study on climate change,” Journal of Cleaner Production, 312.0959-65260959-6526https://bspace.buid.ac.ae/handle/1234/3050https://doi.org/10.1016/j.jclepro.2021.127820.en_USJournal of Cleaner Productionv312 (20210820)oai:bspace.buid.ac.ae:1234/30502026-01-29T15:40:29Z |
| spellingShingle | A novel sentiment analysis framework for monitoring the evolving public opinion in real-time: Case study on climate change El Barachi, May Smart cities Sentiment analysis Opinion leaders Social media analytics Climate change |
| title | A novel sentiment analysis framework for monitoring the evolving public opinion in real-time: Case study on climate change |
| title_full | A novel sentiment analysis framework for monitoring the evolving public opinion in real-time: Case study on climate change |
| title_fullStr | A novel sentiment analysis framework for monitoring the evolving public opinion in real-time: Case study on climate change |
| title_full_unstemmed | A novel sentiment analysis framework for monitoring the evolving public opinion in real-time: Case study on climate change |
| title_short | A novel sentiment analysis framework for monitoring the evolving public opinion in real-time: Case study on climate change |
| title_sort | A novel sentiment analysis framework for monitoring the evolving public opinion in real-time: Case study on climate change |
| topic | Smart cities Sentiment analysis Opinion leaders Social media analytics Climate change |
| url | https://bspace.buid.ac.ae/handle/1234/3050 https://doi.org/10.1016/j.jclepro.2021.127820. |