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Research Article | Open Access |

Effective Opinion Words Extraction for Food Reviews Classification

Author 1: Phuc Quang Tran Author 2: Ngoan Thanh Trieu Author 3: Nguyen Vu Dao Author 4: Hai Thanh Nguyen Author 5: Hiep Xuan Huynh
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 11, No. 7 · Published 2020 · Cited by 14

DOI: https://doi.org/10.14569/IJACSA.2020.0110755

Abstract

Opinion mining (known as sentiment analysis or emotion Artificial Intelligence) holds important roles for e-commerce and benefits to numerous business and organizations. It studies the use of natural language processing, text analysis, computational linguistics, and biometrics to provide us business valuable insights into how people feel about our product brand or service. In this study, we investigate reviews from Amazon Fine Food Reviews dataset including about 500,000 reviews and propose a method to transform reviews into features including Opinion Words which then can be used for reviews classification tasks by machine learning algorithms. From the obtained results, we evaluate useful Opinion Words which can be informative to identify whether the review is positive or negative.

Keywords

How to Cite this Article

Tran, P. Q., Trieu, N. T., Dao, N. V., Nguyen, H. T., & Huynh, H. X. (2020). Effective Opinion Words Extraction for Food Reviews Classification. International Journal of Advanced Computer Science and Applications, 11(7). https://doi.org/10.14569/IJACSA.2020.0110755

Tran, Phuc Quang, et al.. "Effective Opinion Words Extraction for Food Reviews Classification." International Journal of Advanced Computer Science and Applications, vol. 11, no. 7, 2020, https://doi.org/10.14569/IJACSA.2020.0110755.

@article{Tran2020,
  title     = {Effective Opinion Words Extraction for Food Reviews Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {7},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Phuc Quang Tran and Ngoan Thanh Trieu and Nguyen Vu Dao and Hai Thanh Nguyen and Hiep Xuan Huynh},
  doi       = {10.14569/IJACSA.2020.0110755},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110755}
}

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