TY  - JOUR
T1  - KİTLE FONLAMASINDAKİ PROJE METİN İÇERİKLERİNİN LSTM İLE ANALİZİ
TT  - ANALYSIS OF PROJECT TEXT CONTENTS WITH LSTM IN CROWDFUNDING
AU  - Kılınç, Murat
AU  - Aydın, Can
AU  - Tarhan, Çiğdem
PY  - 2022
DA  - March
Y2  - 2022
DO  - 10.54452/jrb.1021694
JF  - Journal of Research in Business
JO  - JRB
PB  - Marmara Üniversitesi
WT  - DergiPark
SN  - 2630-6255
SP  - 48
EP  - 59
VL  - 7
IS  - IMISC2021 Special Issue
LA  - tr
AB  - Kitle fonlaması (KF), topluluklardan gelen fonlamalarla projelerin finanse edilerek hayata geçmesini sağlayan web platformlarıdır. Dünya çapında her yıl bu platformlar kullanılarak binlerce iş fikri çeşitli öznitelikler ile başarılı bir şekilde gerçekleştirilmektedir. KF başarısına en çok etki eden özniteliklerden birisi de projelerdeki metin içerikleridir. Bu doğrultuda yapılan araştırmada, Türkiye’de faaliyet gösteren KF platformlarındaki özetleyici proje metinleri veri kazıma teknikleriyle toplanmış ve analize hazır hale getirilmiştir. Sonrasında ise KF projelerinin metin içerikleri bir RNN modeli olan LSTM kullanılarak başarı etiketleriyle sınıflandırılmış ve değerlendirme metrikleriyle analiz edilmiştir. Parametre seçimleriyle birlikte kurulan modelin doğruluk oranı %96.18’dir. Çalışmanın sonuçları, KF projeleri için hazırlanan metinlerin karar destek sistemlerinde test edilebileceğini göstermektedir.
KW  - kitle Fonlaması
KW  - metin madenciliği
KW  - sinir ağları
KW  - derin öğrenme
KW  - uzun-kısa süreli bellek
N2  - Crowdfunding (CF) are web platforms that enable projects to be funded and implemented with funding from communities. Thousands of business ideas are successfully implemented with various attributes by using these platforms worldwide every year. One of the attributes that most affect the success of CF is the text content in the projects. In the research conducted in this direction, the summary project texts in the CF platforms operating in Turkey were collected by data scraping techniques and made ready for analysis. Afterwards, the text contents of the CF projects were classified with success tags using an RNN model, LSTM, and analyzed with evaluation metrics. The accuracy rate of the model established with the parameter selections is 96.18%. The results of the study show that the texts prepared for CF projects can be tested in decision support systems.
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UR  - https://doi.org/10.54452/jrb.1021694
L1  - https://dergipark.org.tr/tr/download/article-file/2074072
ER  -