Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media
Sentiment Analysis tools allow decision-makers to monitor changes of opinions on social media towards entities, events, products, solutions, and services. These tools provide dashboards for tracking positive, negative, and neutral sentiments for platforms like Twitter where millions of users express...
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2021
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| Online Access: | https://bspace.buid.ac.ae/handle/1234/3043 https://doi.org/10.1109/ACCESS.2021.3073657. |
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| _version_ | 1862980619003232256 |
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| author | ALATTAR , FUAD |
| author2 | SHAALAN, KHALED |
| author2_role | author |
| author_facet | ALATTAR , FUAD SHAALAN, KHALED |
| author_role | author |
| dc.creator.none.fl_str_mv | ALATTAR , FUAD SHAALAN, KHALED |
| dc.date.none.fl_str_mv | 2021 2025-05-14T14:22:34Z 2025-05-14T14:22:34Z |
| dc.identifier.none.fl_str_mv | Alattar, F. and Shaalan, K. (2021) “Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media,” IEEE Access, 9. 2169-3536 https://bspace.buid.ac.ae/handle/1234/3043 https://doi.org/10.1109/ACCESS.2021.3073657. |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | IEEE Accessv9 (2021): 61756-61767 |
| dc.subject.none.fl_str_mv | Emerging Topic Detection, interpreting sentiment variations, opinion reason mining, Sentiment Analysis, Sentiment Reasoning, Sentiment Spikes, Topic Model, Artificial Intelligence, Machine Learning, Filtered-LDA, FB-LDA. |
| dc.title.none.fl_str_mv | Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media |
| dc.type.none.fl_str_mv | Article |
| description | Sentiment Analysis tools allow decision-makers to monitor changes of opinions on social media towards entities, events, products, solutions, and services. These tools provide dashboards for tracking positive, negative, and neutral sentiments for platforms like Twitter where millions of users express their opinions on various topics. However, so far, these tools do not automatically extract reasons for sentiment variations, and that makes it difficult to conclude necessary actions by decision-makers. In this paper, we first compare performance of various Sentiment Analysis classifiers for short texts to select the top performer. Then we present a Filtered-LDA framework that significantly outperformed existing methods of interpreting sentiment variations on Twitter. The framework utilizes cascaded LDA Models with multiple settings of hyperparameters to capture candidate reasons that cause sentiment changes. Then it applies a filter to remove tweets that discuss old topics, followed by a Topic Model with a high Coherence Score to extract Emerging Topics that are interpretable by a human. Finally, a novel Twitter’s sentiment reasoning dashboard is introduced to display the most representative tweet for each candidate reason. |
| id | budr_ba61c93ea4609a8aace92acfb94a1157 |
| identifier_str_mv | Alattar, F. and Shaalan, K. (2021) “Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media,” IEEE Access, 9. 2169-3536 |
| language_invalid_str_mv | en |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/3043 |
| publishDate | 2021 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social MediaALATTAR , FUADSHAALAN, KHALEDEmerging Topic Detection, interpreting sentiment variations, opinion reason mining, Sentiment Analysis, Sentiment Reasoning, Sentiment Spikes, Topic Model, Artificial Intelligence, Machine Learning, Filtered-LDA, FB-LDA.Sentiment Analysis tools allow decision-makers to monitor changes of opinions on social media towards entities, events, products, solutions, and services. These tools provide dashboards for tracking positive, negative, and neutral sentiments for platforms like Twitter where millions of users express their opinions on various topics. However, so far, these tools do not automatically extract reasons for sentiment variations, and that makes it difficult to conclude necessary actions by decision-makers. In this paper, we first compare performance of various Sentiment Analysis classifiers for short texts to select the top performer. Then we present a Filtered-LDA framework that significantly outperformed existing methods of interpreting sentiment variations on Twitter. The framework utilizes cascaded LDA Models with multiple settings of hyperparameters to capture candidate reasons that cause sentiment changes. Then it applies a filter to remove tweets that discuss old topics, followed by a Topic Model with a high Coherence Score to extract Emerging Topics that are interpretable by a human. Finally, a novel Twitter’s sentiment reasoning dashboard is introduced to display the most representative tweet for each candidate reason.IEEE2025-05-14T14:22:34Z2025-05-14T14:22:34Z2021ArticleAlattar, F. and Shaalan, K. (2021) “Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media,” IEEE Access, 9.2169-3536https://bspace.buid.ac.ae/handle/1234/3043https://doi.org/10.1109/ACCESS.2021.3073657.enIEEE Accessv9 (2021): 61756-61767oai:bspace.buid.ac.ae:1234/30432025-05-14T14:25:08Z |
| spellingShingle | Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media ALATTAR , FUAD Emerging Topic Detection, interpreting sentiment variations, opinion reason mining, Sentiment Analysis, Sentiment Reasoning, Sentiment Spikes, Topic Model, Artificial Intelligence, Machine Learning, Filtered-LDA, FB-LDA. |
| title | Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media |
| title_full | Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media |
| title_fullStr | Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media |
| title_full_unstemmed | Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media |
| title_short | Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media |
| title_sort | Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media |
| topic | Emerging Topic Detection, interpreting sentiment variations, opinion reason mining, Sentiment Analysis, Sentiment Reasoning, Sentiment Spikes, Topic Model, Artificial Intelligence, Machine Learning, Filtered-LDA, FB-LDA. |
| url | https://bspace.buid.ac.ae/handle/1234/3043 https://doi.org/10.1109/ACCESS.2021.3073657. |