Public attitudes toward higher education using sentiment analysis and topic modeling
Abstract This study examines higher education through data-mining methodologies, aiming to uncover key themes and sentiments in global discourse. Utilizing sentiment analysis and topic modeling, the research analyzes 157,943 tweets from 84,423 unique users over a five-month period (January to May 20...
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| Format: | Article |
| Language: | English |
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Springer
2024-11-01
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| Series: | Discover Artificial Intelligence |
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| Online Access: | https://doi.org/10.1007/s44163-024-00195-4 |
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| _version_ | 1846165082269024256 |
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| author | Ahmet Göçen Mahat Maalim Ibrahim Asad Ul Islam Khan |
| author_facet | Ahmet Göçen Mahat Maalim Ibrahim Asad Ul Islam Khan |
| author_sort | Ahmet Göçen |
| collection | DOAJ |
| description | Abstract This study examines higher education through data-mining methodologies, aiming to uncover key themes and sentiments in global discourse. Utilizing sentiment analysis and topic modeling, the research analyzes 157,943 tweets from 84,423 unique users over a five-month period (January to May 2023). This period was selected, coinciding with the rise of artificial intelligence (AI) tools, particularly ChatGPT. The study investigates the discussions, emotional tones, and dominant topics shaping the global narrative of higher education within X (Twitter) data. Key findings include the geographical distribution of tweets and the most frequent positive and negative perceptions. It also addresses critical issues such as affordability, accessibility, and funding in higher education. Furthermore, the data shows public reactions to AI in higher education are initially negative, while higher education tweets are primarily characterized by positivity and optimism. The higher education tweets are mainly posted on the weekend, with decreased activity during weekdays. This research provides insights into the evolving higher education landscape amid rapid technological advancements. |
| format | Article |
| id | doaj-art-e658ffc8cc82483e909e5fea97bee6a3 |
| institution | Kabale University |
| issn | 2731-0809 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-e658ffc8cc82483e909e5fea97bee6a32024-11-17T12:38:00ZengSpringerDiscover Artificial Intelligence2731-08092024-11-014111910.1007/s44163-024-00195-4Public attitudes toward higher education using sentiment analysis and topic modelingAhmet Göçen0Mahat Maalim Ibrahim1Asad Ul Islam Khan2Education Faculty, Afyon Kocatepe University School of Humanities and Social Sciences, Ibn Haldun University School of Humanities and Social Sciences, Ibn Haldun UniversityAbstract This study examines higher education through data-mining methodologies, aiming to uncover key themes and sentiments in global discourse. Utilizing sentiment analysis and topic modeling, the research analyzes 157,943 tweets from 84,423 unique users over a five-month period (January to May 2023). This period was selected, coinciding with the rise of artificial intelligence (AI) tools, particularly ChatGPT. The study investigates the discussions, emotional tones, and dominant topics shaping the global narrative of higher education within X (Twitter) data. Key findings include the geographical distribution of tweets and the most frequent positive and negative perceptions. It also addresses critical issues such as affordability, accessibility, and funding in higher education. Furthermore, the data shows public reactions to AI in higher education are initially negative, while higher education tweets are primarily characterized by positivity and optimism. The higher education tweets are mainly posted on the weekend, with decreased activity during weekdays. This research provides insights into the evolving higher education landscape amid rapid technological advancements.https://doi.org/10.1007/s44163-024-00195-4Higher educationText miningTopic modelingX/TwitterSentiment analysisArtificial intelligence |
| spellingShingle | Ahmet Göçen Mahat Maalim Ibrahim Asad Ul Islam Khan Public attitudes toward higher education using sentiment analysis and topic modeling Discover Artificial Intelligence Higher education Text mining Topic modeling X/Twitter Sentiment analysis Artificial intelligence |
| title | Public attitudes toward higher education using sentiment analysis and topic modeling |
| title_full | Public attitudes toward higher education using sentiment analysis and topic modeling |
| title_fullStr | Public attitudes toward higher education using sentiment analysis and topic modeling |
| title_full_unstemmed | Public attitudes toward higher education using sentiment analysis and topic modeling |
| title_short | Public attitudes toward higher education using sentiment analysis and topic modeling |
| title_sort | public attitudes toward higher education using sentiment analysis and topic modeling |
| topic | Higher education Text mining Topic modeling X/Twitter Sentiment analysis Artificial intelligence |
| url | https://doi.org/10.1007/s44163-024-00195-4 |
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