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.
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Acknowledgements
The authors are grateful to the anonymous reviewers for their insightful comments and suggestions which helped improved the quality of the paper. This work was part-supported by the UK Engineering and Physical Sciences Research Council (EPSRC) grant no. EP/M026981/1 (AV-COHGEAR).
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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, vol 463. Springer, Singapore. https://doi.org/10.1007/978-981-10-6571-2_279
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