Abstract In this paper, an efficient model for the Turkish language sentiment analysis has been introduced. As Turkish is an agglutinative language, which requires spatial processing, an efficient pre-processing model was also implemented and integrated as a part of the developed system. In addition, the Deep Convolutional Neural Networks (ConvNet) have been integrated to build an efficient system. Several experiments using the “Turkish movie reviews” dataset have been conducted, and it has been observed that the developed system has improved the sentiment analysis system that supports the Turkish language and significantly outperforms the existing state-of-the-art Turkish sentiment analysis systems.

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[30] Zhang L. and Chen C., “Sentiment Classification with Convolutional Neural Networks: An Experimental Study on A Large-Scale Chinese Conversation Corpus,” in Proceedings of the 12th International Conference on Computational Intelligence and Security, Wuxi, pp. 165-169, 2016. Saed Alqaraleh is an Assistant Professor at Hasan Kalyoncu University, Gaziantep, Turkey. He completed his Ph.D. in Computer Engineering (2015) from Eastern Mediterranean University, Cyprus. His present areas of research include Information retrieval and security, Natural Language Processing, Visual Place Recognition, and Machine learning.