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Computational Intelligence and Neuroscience
Volume 2018 (2018), Article ID 9293437, 12 pages
Research Article

Automated Text Analysis Based on Skip-Gram Model for Food Evaluation in Predicting Consumer Acceptance

1Department of Food Science and Biotechnology, Sejong University, Seoul, Republic of Korea
2Department of Computer Science and Engineering, Sejong University, Seoul, Republic of Korea

Correspondence should be addressed to Hyeonjoon Moon

Received 2 June 2017; Revised 27 July 2017; Accepted 7 November 2017; Published 22 January 2018

Academic Editor: Elio Masciari

Copyright © 2018 Augustine Yongwhi Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The purpose of this paper is to evaluate food taste, smell, and characteristics from consumers’ online reviews. Several studies in food sensory evaluation have been presented for consumer acceptance. However, these studies need taste descriptive word lexicon, and they are not suitable for analyzing large number of evaluators to predict consumer acceptance. In this paper, an automated text analysis method for food evaluation is presented to analyze and compare recently introduced two jjampong ramen types (mixed seafood noodles). To avoid building a sensory word lexicon, consumers’ reviews are collected from SNS. Then, by training word embedding model with acquired reviews, words in the large amount of review text are converted into vectors. Based on these words represented as vectors, inference is performed to evaluate taste and smell of two jjampong ramen types. Finally, the reliability and merits of the proposed food evaluation method are confirmed by a comparison with the results from an actual consumer preference taste evaluation.