KİTLE FONLAMASINDAKİ PROJE METİN İÇERİKLERİNİN LSTM İLE ANALİZİ

Yıl 2022, Cilt: 7 Sayı: IMISC2021 Special Issue, 48 - 59, 30.03.2022

Murat Kılınç , Can Aydın , Çiğdem Tarhan

https://doi.org/10.54452/jrb.1021694

Öz

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.

Anahtar Kelimeler

kitle Fonlaması, metin madenciliği, sinir ağları, derin öğrenme, uzun-kısa süreli bellek

Destekleyen Kurum

TÜBİTAK

Proje Numarası

121E363

Teşekkür

Bu araştırma TÜBİTAK tarafından 121E363 proje numarasıyla desteklenmiştir.

ANALYSIS OF PROJECT TEXT CONTENTS WITH LSTM IN CROWDFUNDING

Öz

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.

Anahtar Kelimeler

Crowdfunding, text mining, neural networks, deep learning, long-short term memory

Proje Numarası

121E363

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