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Deep Sentiment Anal...
Deep Sentiment Analysis : A Case Study on Stemmed Turkish Twitter Data
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- Shehu, Harisu Abdullahi (författare)
- Victoria University of Wellington, NZL
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- Sharif, Md. Haidar (författare)
- University of Hail, SAU
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- Sharif, Md. Haris Uddin (författare)
- University of the Cumberlands, USA
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- Datta, Ripon (författare)
- University of the Cumberlands, USA
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- Tokat, Sezai (författare)
- Pamukkale University, TUR
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- Uyaver, Sahin (författare)
- Turkish-German University, TUR
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- Kusetogullari, Hüseyin, 1981- (författare)
- Blekinge Tekniska Högskola,Institutionen för datavetenskap
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- Ramadan, Rabie A. (författare)
- Cairo University, EGY
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers Inc. 2021
- 2021
- Engelska.
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers Inc.. - 2169-3536. ; 9, s. 56836-56854
- Relaterad länk:
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https://doi.org/10.1...
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https://bth.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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. CCBY
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- Analytical models
- Blogs
- Data augmentation
- Deep learning
- Machine learning
- Machine learning algorithms
- Neural networks
- Recurrent neural networks
- Sentiment analysis
- Social networking (online)
- Sociology
- Turkish
- Twitter
- Classification (of information)
- Learning systems
- Augmentation techniques
- Convolution neural network
- Performance factors
- Performance rankings
- Recurrent neural network (RNN)
- Research topics
- Training time
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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