Determining the orientation in choosing furniture based on social media based on data mining algorithms: Twitter example
In parallel with the increase in internet usage, people from different parts of the world can easily convey their thoughts and feelings on social issues through social media. Millions of messages are written and read every day on various topics on a global scale through Twitter, which has an importa...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Isparta University of Applied Sciences Faculty of Forestry
2019-12-01
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| Series: | Turkish Journal of Forestry |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/pub/tjf/issue/51103/609967?publisher=iubu |
| _version_ | 1797913543409926144 |
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| author | Selman KARAYILMAZLAR Timuçin BARDAK Özkan AVCI Kadir KAYAHAN Atakan Süha KARAYILMAZLAR Yıldız ÇABUK Rıfat KURT Erol İMREN |
| author_facet | Selman KARAYILMAZLAR Timuçin BARDAK Özkan AVCI Kadir KAYAHAN Atakan Süha KARAYILMAZLAR Yıldız ÇABUK Rıfat KURT Erol İMREN |
| author_sort | Selman KARAYILMAZLAR |
| collection | DOAJ |
| description | In parallel with the increase in internet usage, people from different parts of the world can easily convey their thoughts and feelings on social issues through social media. Millions of messages are written and read every day on various topics on a global scale through Twitter, which has an important place in these social media. While it is important to understand consumer behaviors in order to increase the competitiveness of firms, big data sources such as Twitter have multi-faceted the methods of analyzing behaviors. At the same time, developed countries allocate significant resources to data mining projects in order to have power. The use of Twitter and data mining as an alternative data source to identify trends in furniture choice has been proposed. The popular tweets with furniture using the Rapidminer and natural language processing software were gathered for ten months between May 2018 and February 2019, and natural language processing software enabled us to determine the mood of the tweets (positive and negative). Morphological analysis of the keywords in positive and negative tweets was then performed. Finally, meaningful information was obtained by utilizing the decision tree and association algorithms used in data mining. According to the decision tree algorithm, the most dominant words in the formation of positive or negative emotions were the challenge, campaign, discover and idea. As a result of the syntax of association, the most positive emotions were made with the order of words that awaken the emotions, and the opportunity was found as wood. In the same algorithm, the words that awaken the most negative emotions were listed as gloom, seedy, uncomfortable and fabric. |
| first_indexed | 2024-04-10T12:14:20Z |
| format | Article |
| id | doaj.art-3b03a085c4f14d93bdb323e6847e7855 |
| institution | Directory Open Access Journal |
| issn | 2149-3898 |
| language | English |
| last_indexed | 2024-04-10T12:14:20Z |
| publishDate | 2019-12-01 |
| publisher | Isparta University of Applied Sciences Faculty of Forestry |
| record_format | Article |
| series | Turkish Journal of Forestry |
| spelling | doaj.art-3b03a085c4f14d93bdb323e6847e78552023-02-15T16:15:51ZengIsparta University of Applied Sciences Faculty of ForestryTurkish Journal of Forestry2149-38982019-12-0120444745710.18182/tjf.6099671656Determining the orientation in choosing furniture based on social media based on data mining algorithms: Twitter exampleSelman KARAYILMAZLAR0Timuçin BARDAK1Özkan AVCI2Kadir KAYAHAN3Atakan Süha KARAYILMAZLAR4Yıldız ÇABUK5Rıfat KURT6Erol İMREN7BARTIN UNIVERSITYBARTIN UNIVERSITYBARTIN UNIVERSITYBARTIN UNIVERSITYBARTIN UNIVERSITYBARTIN UNIVERSITYBARTIN UNIVERSITYBARTIN UNIVERSITYIn parallel with the increase in internet usage, people from different parts of the world can easily convey their thoughts and feelings on social issues through social media. Millions of messages are written and read every day on various topics on a global scale through Twitter, which has an important place in these social media. While it is important to understand consumer behaviors in order to increase the competitiveness of firms, big data sources such as Twitter have multi-faceted the methods of analyzing behaviors. At the same time, developed countries allocate significant resources to data mining projects in order to have power. The use of Twitter and data mining as an alternative data source to identify trends in furniture choice has been proposed. The popular tweets with furniture using the Rapidminer and natural language processing software were gathered for ten months between May 2018 and February 2019, and natural language processing software enabled us to determine the mood of the tweets (positive and negative). Morphological analysis of the keywords in positive and negative tweets was then performed. Finally, meaningful information was obtained by utilizing the decision tree and association algorithms used in data mining. According to the decision tree algorithm, the most dominant words in the formation of positive or negative emotions were the challenge, campaign, discover and idea. As a result of the syntax of association, the most positive emotions were made with the order of words that awaken the emotions, and the opportunity was found as wood. In the same algorithm, the words that awaken the most negative emotions were listed as gloom, seedy, uncomfortable and fabric.https://dergipark.org.tr/tr/pub/tjf/issue/51103/609967?publisher=iubufurnituresocial mediatwitterdatasentiment analysismobilyasosyal medyatwiteerveriduygu analizi |
| spellingShingle | Selman KARAYILMAZLAR Timuçin BARDAK Özkan AVCI Kadir KAYAHAN Atakan Süha KARAYILMAZLAR Yıldız ÇABUK Rıfat KURT Erol İMREN Determining the orientation in choosing furniture based on social media based on data mining algorithms: Twitter example Turkish Journal of Forestry furniture social media data sentiment analysis mobilya sosyal medya twiteer veri duygu analizi |
| title | Determining the orientation in choosing furniture based on social media based on data mining algorithms: Twitter example |
| title_full | Determining the orientation in choosing furniture based on social media based on data mining algorithms: Twitter example |
| title_fullStr | Determining the orientation in choosing furniture based on social media based on data mining algorithms: Twitter example |
| title_full_unstemmed | Determining the orientation in choosing furniture based on social media based on data mining algorithms: Twitter example |
| title_short | Determining the orientation in choosing furniture based on social media based on data mining algorithms: Twitter example |
| title_sort | determining the orientation in choosing furniture based on social media based on data mining algorithms twitter example |
| topic | furniture social media data sentiment analysis mobilya sosyal medya twiteer veri duygu analizi |
| url | https://dergipark.org.tr/tr/pub/tjf/issue/51103/609967?publisher=iubu |
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