Detection of Customer Opinions with Deep Learning Method for Metaverse Collaborating Brands
| dc.contributor.author | Aygun I. | |
| dc.contributor.author | Kaya B. | |
| dc.contributor.author | Kaya M. | |
| dc.date.accessioned | 2024-07-22T08:04:56Z | |
| dc.date.available | 2024-07-22T08:04:56Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | In recent years, metaverse projects have been developed that both increase the number of users and bring a new concept to the use of the internet. With this development, collaborations are frequently established within the business world with metaverse projects that attract the attention of companies. In the study, the gains of the companies operating in the metaverse after these activities were examined. Thanks to the tweets collected before and after the companies participated in the metaverse, it was analyzed how potential users interpreted their participation in the metaverse. In this context, sentiment analysis experiments were conducted for five different clothing, sportswear, and retail companies (Adidas, Balenciaga, H&M, Nike, and Zara) serving in similar fields of activity. The BERT architecture, which is a language representation model, was used in the experiments, and it was seen that the positive shares on Twitter for companies increased greatly. After the companies transitioned to Metaverse, the biggest change in positive Twitter posts was seen in Nike, with 47%, and in second place, positive Twitter posts about Balenciaga increased by 42%. Experiments show that firms' assets in the metaverse create a positive perception within one month. © 2022 IEEE. | |
| dc.identifier.DOI-ID | 10.1109/ICDABI56818.2022.10041681 | |
| dc.identifier.uri | http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/12910 | |
| dc.language.iso | English | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.subject | Deep learning | |
| dc.subject | Learning systems | |
| dc.subject | Social networking (online) | |
| dc.subject | Advertizing | |
| dc.subject | BERT | |
| dc.subject | Big changes | |
| dc.subject | Learning methods | |
| dc.subject | Metaverses | |
| dc.subject | Potential users | |
| dc.subject | Representation model | |
| dc.subject | Sentiment analysis | |
| dc.subject | Text-mining | |
| dc.subject | Twitter posts | |
| dc.subject | Sentiment analysis | |
| dc.title | Detection of Customer Opinions with Deep Learning Method for Metaverse Collaborating Brands | |
| dc.type | Conference paper |