발간년도 : [2019]
| 논문정보 |
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| 논문명(한글) |
[Vol.14, No.4] A Study of User Text Sentiment Dictionary for Food Recommendation Service on Big Data Environment |
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| 논문투고자 |
Jin-Kwan Cho |
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| 논문내용 |
In general, it is practice to assess food tastes based on sensory tests, however, this method has a considerable disadvantage in which it is pricey and need to required more time. In addition, important disparity appear in relying on each evaluator. We make a good food tastes up, we are necessary to process as following; Firstly, a pre-taste User text sentiment dictionary is based on establishment a kind of food studies and then gather this information on twitter data of social network service, internet and social media so on. Secondary, many information are based on anticipate in original food tastes data by web-based and mobile-based from their systems. Food name of providing on social network service and internet is different each of names with their food tastes. After all, This data is divided into four different names look over stemming that new savors and foods words are found in order to add keyword to taste sentiment dictionary. Accordingly, frequency measurement of newly formated taste keyword is important based on sensitivity of filtering data by utilizing a pre-taste word emotional dictionary determined by the weight of the food taste keyword and it also illustrates the taste of foods by taste dictionary. |
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