![](/images/graphics-bg.png)
Automated Text Analysis Based on Skip-Gram Model for Food Evaluation in Predicting Consumer Acceptance
Joint Authors
Kim, Augustine Yongwhi
Ha, Jin Gwan
Choi, Hoduk
Moon, Hyeonjoon
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-22
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Kim, Augustine Yongwhi& Ha, Jin Gwan& Choi, Hoduk& Moon, Hyeonjoon. 2018. Automated Text Analysis Based on Skip-Gram Model for Food Evaluation in Predicting Consumer Acceptance. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1130852
Modern Language Association (MLA)
Kim, Augustine Yongwhi…[et al.]. Automated Text Analysis Based on Skip-Gram Model for Food Evaluation in Predicting Consumer Acceptance. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1130852
American Medical Association (AMA)
Kim, Augustine Yongwhi& Ha, Jin Gwan& Choi, Hoduk& Moon, Hyeonjoon. Automated Text Analysis Based on Skip-Gram Model for Food Evaluation in Predicting Consumer Acceptance. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1130852
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130852