Detecting Topics and Polarity From Twitter: A University Faculty Case
Social networks have become a powerful communication tool, with millions of people exchanging information, opinions, and experiences daily. Companies, organizations, and even people have turned this tool into a marketing platform to position themselves and gain popularity. However, not only do compa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10373020/ |
| _version_ | 1797352860407562240 |
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| author | Almudena Sanchez Ruiz Daniel Galan Angel Garcia-Beltran Javier Rodriguez-Vidal |
| author_facet | Almudena Sanchez Ruiz Daniel Galan Angel Garcia-Beltran Javier Rodriguez-Vidal |
| author_sort | Almudena Sanchez Ruiz |
| collection | DOAJ |
| description | Social networks have become a powerful communication tool, with millions of people exchanging information, opinions, and experiences daily. Companies, organizations, and even people have turned this tool into a marketing platform to position themselves and gain popularity. However, not only do companies present products or services to society, but society also provides feedback. This feedback also has a significant impact. It is impossible to process all this vast information manually in time, but it is crucial. This information is precious even to governmental or public entities such as universities. Potential future students will use social media to learn about the general feel of the institution. Therefore, this study presents a new dataset called CEIMaT2021, which compiles all tweets in Spanish related to the Technical School of Industrial Engineering of the Universidad Politécnica de Madrid (ETSII-UPM). This dataset is designed for two main tasks of Online Reputation Management: 1) automatic detection of topics and 2) polarity. Furthermore, this study shows that the BETO model obtains better performance for topic detection for these tasks. Meanwhile, the MarIA model obtains better results for polarity detection. |
| first_indexed | 2024-03-08T13:22:49Z |
| format | Article |
| id | doaj.art-9ae2bff66886435c884c84403490b537 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T13:22:49Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9ae2bff66886435c884c84403490b5372024-01-18T00:01:16ZengIEEEIEEE Access2169-35362024-01-011214815610.1109/ACCESS.2023.334667510373020Detecting Topics and Polarity From Twitter: A University Faculty CaseAlmudena Sanchez Ruiz0Daniel Galan1https://orcid.org/0000-0003-3078-3643Angel Garcia-Beltran2https://orcid.org/0000-0003-1900-0222Javier Rodriguez-Vidal3https://orcid.org/0000-0002-9006-9639Departamento de Automática, , Ingeniería Eléctrica y Electrónica e Informática Industrial, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, Madrid, SpainCentre for Automation and Robotics (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, SpainDepartamento de Automática, , Ingeniería Eléctrica y Electrónica e Informática Industrial, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, Madrid, SpainDepartamento de Automática, , Ingeniería Eléctrica y Electrónica e Informática Industrial, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, Madrid, SpainSocial networks have become a powerful communication tool, with millions of people exchanging information, opinions, and experiences daily. Companies, organizations, and even people have turned this tool into a marketing platform to position themselves and gain popularity. However, not only do companies present products or services to society, but society also provides feedback. This feedback also has a significant impact. It is impossible to process all this vast information manually in time, but it is crucial. This information is precious even to governmental or public entities such as universities. Potential future students will use social media to learn about the general feel of the institution. Therefore, this study presents a new dataset called CEIMaT2021, which compiles all tweets in Spanish related to the Technical School of Industrial Engineering of the Universidad Politécnica de Madrid (ETSII-UPM). This dataset is designed for two main tasks of Online Reputation Management: 1) automatic detection of topics and 2) polarity. Furthermore, this study shows that the BETO model obtains better performance for topic detection for these tasks. Meanwhile, the MarIA model obtains better results for polarity detection.https://ieeexplore.ieee.org/document/10373020/Datasetinformation retrievalpolaritysocial network analysistopicTwitter |
| spellingShingle | Almudena Sanchez Ruiz Daniel Galan Angel Garcia-Beltran Javier Rodriguez-Vidal Detecting Topics and Polarity From Twitter: A University Faculty Case IEEE Access Dataset information retrieval polarity social network analysis topic |
| title | Detecting Topics and Polarity From Twitter: A University Faculty Case |
| title_full | Detecting Topics and Polarity From Twitter: A University Faculty Case |
| title_fullStr | Detecting Topics and Polarity From Twitter: A University Faculty Case |
| title_full_unstemmed | Detecting Topics and Polarity From Twitter: A University Faculty Case |
| title_short | Detecting Topics and Polarity From Twitter: A University Faculty Case |
| title_sort | detecting topics and polarity from twitter a university faculty case |
| topic | Dataset information retrieval polarity social network analysis topic |
| url | https://ieeexplore.ieee.org/document/10373020/ |
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