Abstract
Opinion mining is the use of analytic methods to extract subjective information. A study was conducted to apply spatial opinion mining in literary works to examine the writers’ opinions about how matters of space and place are experienced. For this reason, this paper conducts a review study to identify and compare different analytical techniques for opinion mining in fictional writings. This review study focused on sentiment analysis and topic modeling as two main techniques for spatial opinion mining in literary works. The comparison results are reported and the limitations of different techniques are mentioned. The results of this study can assist researchers in the field of opinion and text mining.
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Acknowledgment
The authors are thankful to School of Computer Sciences and School of Humanities, Universiti Sains Malaysia for unlimited supports to finish this project. In addition, the authors are grateful to Division of Research & Innovation, USM for financial support from Short Term Grant (304/PHUMANITI/6315300) granted to Dr Moussa Pourya Asl.
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Ying, S.Y., Keikhosrokiani, P., Asl, M.P. (2021). Comparison of Data Analytic Techniques for a Spatial Opinion Mining in Literary Works: A Review Paper. In: Saeed, F., Mohammed, F., Al-Nahari, A. (eds) Innovative Systems for Intelligent Health Informatics. IRICT 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-030-70713-2_49
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