Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/29408
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Alqarafi, Abdulrahman
Adeel, Ahsan
Hawalah, Ahmed
Swingler, Kevin
Hussain, Amir
Title: A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter
Editor(s): Hussain, A
Zhao, H
Ren, J
Zheng, J
Liu, C-L
Luo, B
Zhao, X
Citation: Alqarafi A, Adeel A, Hawalah A, Swingler K & Hussain A (2018) A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter. In: Hussain A, Zhao H, Ren J, Zheng J, Liu C, Luo B & Zhao X (eds.) Advances in Brain Inspired Cognitive Systems. Lecture Notes in Computer Science, 10989. BICS 2018: 9th International Conference on Brain Inspired Cognitive Systems, Xi'an, China, 07.07.2018-08.07.2018. Cham, Switzerland: Springer International Publishing, pp. 589-596. https://doi.org/10.1007/978-3-030-00563-4_57
Issue Date: 2018
Series/Report no.: Lecture Notes in Computer Science, 10989
Conference Name: BICS 2018: 9th International Conference on Brain Inspired Cognitive Systems
Conference Dates: 2018-07-07 - 2018-07-08
Conference Location: Xi'an, China
Abstract: In the literature, limited work has been conducted to develop sentiment resources for Saudi dialect. The lack of resources such as dialectical lexicons and corpora are some of the major bottlenecks to the successful development of Arabic sentiment analysis models. In this paper, a semi-supervised approach is presented to construct an annotated sentiment corpus for Saudi dialect using Twitter. The presented approach is primarily based on a list of lexicons built by using word embedding techniques such as word2vec. A huge corpus extracted from twitter is annotated and manually reviewed to exclude incorrect annotated tweets which is publicly available. For corpus validation, state-of-the-art classification algorithms (such as Logistic Regression, Support Vector Machine, and Naive Bayes) are applied and evaluated. Simulation results demonstrate that the Naive Bayes algorithm outperformed all other approaches and achieved accuracy up to 91%.
Status: AM - Accepted Manuscript
Rights: This is a post-peer-review, pre-copyedit version of a paper published in Hussain A, Zhao H, Ren J, Zheng J, Liu C, Luo B & Zhao X (eds.) Advances in Brain Inspired Cognitive Systems. Lecture Notes in Computer Science, 10989. BICS 2018: 9th International Conference on Brain Inspired Cognitive Systems, Xi'an, China, 07.07.2018-08.07.2018. Cham, Switzerland: Springer International Publishing, pp. 589-596. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-00563-4_57

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