Please use this identifier to cite or link to this item:
https://hdl.handle.net/11499/47574
Title: | Deep Sentiment Analysis: a Case Study on Stemmed Turkish Twitter Data | Authors: | Shehu, Harisu Abdullahi Sharif, Md. Haidar Sharif, Md. Haris Uddin Datta, Ripon Tokat, Sezai Uyaver, Sahin Ramadan, Rabie A. |
Keywords: | Social Networking (Online) Blogs Sentiment Analysis Analytical Models Recurrent Neural Networks Sociology Machine Learning Algorithms Data Augmentation Deep Learning Machine Learning Neural Networks Sentiment Analysis Turkish |
Publisher: | Ieee-inst Electrical Electronics Engineers inc | Abstract: | Sentiment analysis using stemmed Twitter data from various languages is an emerging research topic. In this paper, we address three data augmentation techniques namely Shift, Shuffle, and Hybrid to increase the size of the training data; and then we use three key types of deep learning (DL) models namely recurrent neural network (RNN), convolution neural network (CNN), and hierarchical attention network (HAN) to classify the stemmed Turkish Twitter data for sentiment analysis. The performance of these DL models has been compared with the existing traditional machine learning (TML) models. The performance of TML models has been affected negatively by the stemmed data, but the performance of DL models has been improved greatly with the utilization of the augmentation techniques. Based on the simulation, experimental, and statistical results analysis deeming identical datasets, it has been concluded that the TML models outperform the DL models with respect to both training-time (TTM) and runtime (RTM) complexities of the algorithms; but the DL models outperform the TML models with respect to the most important performance factors as well as the average performance rankings. | Description: | Shehu, Harisu Abdullahi/0000-0002-9689-3290; Kusetogullari, Huseyin/0000-0001-5762-6678; Tokat, Sezai/0000-0003-0193-8220; Sharif, Md Haris Uddin/0000-0002-1169-8438; Uyaver, Sahin/0000-0001-8776-3032; Sharif, Md. Haidar/0000-0001-7235-6004; Ramadan, Rabie/0000-0002-0281-9381; Datta, Ripon/0000-0003-4738-2918 | URI: | https://doi.org/10.1109/ACCESS.2021.3071393 | ISSN: | 2169-3536 |
Appears in Collections: | Mühendislik Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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