Perceptions of the Future of Artificial Intelligence on Social Media: A Topic Modeling and Sentiment Analysis Approach
Today’s AI technology has various applications in many fields, thus creating opportunities to improve different aspects of daily life and optimize business operations. However, there are also societal expectations and concerns regarding AI and its future impacts. Investigating such societ...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10772463/ |
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| author | Ayse Ocal |
| author_facet | Ayse Ocal |
| author_sort | Ayse Ocal |
| collection | DOAJ |
| description | Today’s AI technology has various applications in many fields, thus creating opportunities to improve different aspects of daily life and optimize business operations. However, there are also societal expectations and concerns regarding AI and its future impacts. Investigating such societal opinions and feelings is essential for social acceptance, further development and distribution of such technology, regulation, and adaptation to changes and policies. Despite this situation, such an exploration has not been sufficiently conducted in the existing literature and the most appropriate methods for such an exploration have not been sufficiently investigated. To contribute to addressing this limitation in literature, this study applies topic modeling and sentiment analysis approaches to investigate societal opinions and feelings about the future of AI on social media, which includes conversations from various segments of society. A corpus consisting of 16,611 comments and 998 unique Reddit post titles was analyzed with a customized BERTopic model for topic modeling and a BERT sentiment classification model. This study highlights the significant advantages of using BERTopic and BERT models in analyzing a large sample of social media discussions. The results of this study can help realize the potential of text analytics methods through transformer-based language models to derive empirical findings from large-scale data samples. |
| format | Article |
| id | doaj-art-4adc3f7d2ed04c62818f10e4acc739e1 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4adc3f7d2ed04c62818f10e4acc739e12025-08-20T02:48:46ZengIEEEIEEE Access2169-35362024-01-011218238618240910.1109/ACCESS.2024.351052610772463Perceptions of the Future of Artificial Intelligence on Social Media: A Topic Modeling and Sentiment Analysis ApproachAyse Ocal0https://orcid.org/0000-0002-1925-4305Department of Computer Engineering, Yildiz Technical University, İstanbul, TürkiyeToday’s AI technology has various applications in many fields, thus creating opportunities to improve different aspects of daily life and optimize business operations. However, there are also societal expectations and concerns regarding AI and its future impacts. Investigating such societal opinions and feelings is essential for social acceptance, further development and distribution of such technology, regulation, and adaptation to changes and policies. Despite this situation, such an exploration has not been sufficiently conducted in the existing literature and the most appropriate methods for such an exploration have not been sufficiently investigated. To contribute to addressing this limitation in literature, this study applies topic modeling and sentiment analysis approaches to investigate societal opinions and feelings about the future of AI on social media, which includes conversations from various segments of society. A corpus consisting of 16,611 comments and 998 unique Reddit post titles was analyzed with a customized BERTopic model for topic modeling and a BERT sentiment classification model. This study highlights the significant advantages of using BERTopic and BERT models in analyzing a large sample of social media discussions. The results of this study can help realize the potential of text analytics methods through transformer-based language models to derive empirical findings from large-scale data samples.https://ieeexplore.ieee.org/document/10772463/Artificial intelligenceBERTBERTopicsentiment analysistopic modeling |
| spellingShingle | Ayse Ocal Perceptions of the Future of Artificial Intelligence on Social Media: A Topic Modeling and Sentiment Analysis Approach IEEE Access Artificial intelligence BERT BERTopic sentiment analysis topic modeling |
| title | Perceptions of the Future of Artificial Intelligence on Social Media: A Topic Modeling and Sentiment Analysis Approach |
| title_full | Perceptions of the Future of Artificial Intelligence on Social Media: A Topic Modeling and Sentiment Analysis Approach |
| title_fullStr | Perceptions of the Future of Artificial Intelligence on Social Media: A Topic Modeling and Sentiment Analysis Approach |
| title_full_unstemmed | Perceptions of the Future of Artificial Intelligence on Social Media: A Topic Modeling and Sentiment Analysis Approach |
| title_short | Perceptions of the Future of Artificial Intelligence on Social Media: A Topic Modeling and Sentiment Analysis Approach |
| title_sort | perceptions of the future of artificial intelligence on social media a topic modeling and sentiment analysis approach |
| topic | Artificial intelligence BERT BERTopic sentiment analysis topic modeling |
| url | https://ieeexplore.ieee.org/document/10772463/ |
| work_keys_str_mv | AT ayseocal perceptionsofthefutureofartificialintelligenceonsocialmediaatopicmodelingandsentimentanalysisapproach |