KaydedilenlerDerginizi ekleyin

Dergiler / Sigma Mühendislik ve Fen Bilimleri Dergisi / 2020 / Cilt: 38 - Sayı: 3

CLASSIFICATION OF TURKISH TWEETS BY DOCUMENT VECTORS AND INVESTIGATION OF THE EFFECTS OF PARAMETER CHANGES ON CLASSIFICATION SUCCESS

Metin BİLGİN
Sayfa
1581–1592
Yayın Tarihi
2021-10-05
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

It was found in the study that as well as higher accuracy values were obtained by the DBoW (Distributed Bag of Words) method than DM (Distributed Memory) method; higher accuracy values were also obtained by DBoW-NS (Negative Sampling) architecture than others.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Natural language processing is an artificial intelligence field which is gaining in popularity in recent years.
📊
Temel Bulgular & Veri:To make an emotional deduction from texts related to an issue, or classify documents are of great importance considering the increasing data size in today's world.
💡
Sonuç & Katkı:It was found in the study that as well as higher accuracy values were obtained by the DBoW (Distributed Bag of Words) method than DM (Distributed Memory) method; higher accuracy values were also obtained by DBoW-NS (Negative Sampling) architecture than others.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Text classification · natural language processing · document vectors

Abstract

Natural language processing is an artificial intelligence field which is gaining in popularity in recent years. To make an emotional deduction from texts related to an issue, or classify documents are of great importance considering the increasing data size in today's world. Understanding and interpreting written texts is a feature that pertains to people. But, it is possible to deduce from texts or classify texts using natural language processing which is a sub-branch of machine learning and artificial intelligence. In this study, both text classification was made on Turkish tweets, and text classification success of method parameter changes was investigated using two different methods of the algorithm mentioned as document vectors in the literature. It was found in the study that as well as higher accuracy values were obtained by the DBoW (Distributed Bag of Words) method than DM (Distributed Memory) method; higher accuracy values were also obtained by DBoW-NS (Negative Sampling) architecture than others.

🔖

Bu Makaleye Atıf Yap

BİLGİN, M. (2021). CLASSIFICATION OF TURKISH TWEETS BY DOCUMENT VECTORS AND INVESTIGATION OF THE EFFECTS OF PARAMETER CHANGES ON CLASSIFICATION SUCCESS. Sigma Mühendislik ve Fen Bilimleri Dergisi, Cilt: 38 - Sayı: 3, 1581–1592. https://ajindex.com/articles/419092

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (15)

  1. Chowdhury. G.G., “Natural language processing”, Annual review of information science and technology,37,1, 51-89, 2005
  2. Maron, M.E., “Automatic indexing: an experimental inquiry”, Journal of the ACM,8,3,404-417,1961
  3. Fabrizio, S., “Machine learning in automated text categorization”, ACM computing surveys,34,1,1-47,2001
  4. Dalal, M.K., Mukesh, A. Z., “Automatic Text Classification: A Technical Review”, International Journal of Computer Applications, 28,2,37- 40, 2011
  5. Sommer, S.,Schieber, A., Hilbert, A., Heinrich K., “Analyzing customer sentiments in microblogs–A topic-model-based approach for Twitter datasets”, Americas conference on information systems (AMCIS),Detroit, Michigan, USA, (2011), 1-7
  6. Liu, B, Lei, Z., “Mining Text Data: A survey of opinion mining and sentiment analysis”, Mining Text Data, Springer, Boston, USA, 2012, 415-463
  7. Prabowo, R., Thelwall, M., “Sentiment analysis: A combined approach”, Journal of Informetrics, 3,2, 143-157, 2009
  8. Zhang, D., Xu H., Su, Z., Xu, Y., “Chinese comments sentiment classification based on word2vec and SVMperf”, Expert Systems with Applications, 42,4,1857-1863,2014
  9. Dickinson, B., Wei, H., “Sentiment analysis of investor opinions on twitter”, Social Networking, 4,3, 62-71,2015
  10. Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B., “Learning sentiment-specific word embedding for twitter sentiment classification”, 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, Maryland, USA, (2014), 1555-1565
  11. Polpinij. J., Natthakit, S., Paphonput, S., “Word2Vec Approach for Sentiment Classification Relating to Hotel Reviews”, 13th. International Conference on Computing and Information Technology, Bangkok, Thailand, (2017), 308-316
  12. Şahin, G., “Turkish document classification based on Word2Vec and SVM classifier”, Signal Processing and Communications Applications Conference, Antalya, Turkey, (2017), 1-4
  13. Xue, B., Chen, F. and Zhan, S., “A study on sentiment computing and classification of sina weibo with word2vec”, IEEE International Congress on Big Data, Alaska, USA, (2014), 358-363
  14. Bilgin, M. and Şentürk, İ. F., “Sentiment analysis on Twitter data with semi-supervised Doc2Vec”, 2nd. International Conference on Computer Science and Engineering, Antalya, Turkey, (2017), 661-666
  15. Bilgin, M. and Köktaş, H., “Word2Vec Based Sentiment Analysis for Turkish Texts”, International Conference on Engineering Technologies, Konya, Turkey,