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
Twitter
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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