Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/27774
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Author(s): Dashtipour, Kia
Gogate, Mandar
Adeel, Ahsan
Hussain, Amir
Alqarafi, Abdulrahman
Durrani, Tariq
Title: A comparative study of Persian sentiment analysis based on different feature combinations
Editor(s): Liang, Q
Mu, J
Jia, M
Wang, W
Feng, X
Zhang, B
Citation: Dashtipour K, Gogate M, Adeel A, Hussain A, Alqarafi A & Durrani T (2019) A comparative study of Persian sentiment analysis based on different feature combinations. In: Liang Q, Mu J, Jia M, Wang W, Feng X & Zhang B (eds.) Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, 463. CSPS 2017: Communications, Signal Processing, and System, Harbin, China, 14.07.2017-16.07.2017. Cham, Switzerland: Springer, pp. 2288-2294. https://doi.org/10.1007/978-981-10-6571-2_279
Issue Date: 2019
Date Deposited: 10-Sep-2018
Series/Report no.: Lecture Notes in Electrical Engineering, 463
Conference Name: CSPS 2017: Communications, Signal Processing, and System
Conference Dates: 2017-07-14 - 2017-07-16
Conference Location: Harbin, China
Abstract: In recent years, the use of internet and correspondingly the number of online reviews, comments and opinions have increased significantly. It is indeed very difficult for humans to read these opinions and classify them accurately. Consequently, there is a need for an automated system to process this big data. In this paper, a novel sentiment analysis framework for Persian language has been proposed. The proposed framework comprises three basic steps: pre-processing, feature extraction, and support vector machine (SVM) based classification. The performance of the proposed framework has been evaluated taking into account different features combinations. The simulation results have revealed that the best performance could be achieved by integrating unigram, bigram, and trigram features.
Status: AM - Accepted Manuscript
Rights: This is a post-peer-review, pre-copyedit version of an paper published in Liang Q, Mu J, Jia M, Wang W, Feng X & Zhang B (eds.) Communications, Signal Processing, and Systems. CSPS 2017. The final authenticated version is available online at: https://doi.org/Liang Q, Mu J, Jia M, Wang W, Feng X & Zhang B (eds.) Communications, Signal Processing, and Systems. CSPS 2017

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