Social Media Mining for an Analysis of Nutrition and Dietary Health in Taiwan.

Yu-Lun Hsieh,Wei-Yu Chang,Yung-Chun Chang

doi:10.3390/nu13061778

Abstract

Dining is an essential part of human life. In order to pursue a healthier self, more and more people enjoy homemade cuisines. Consequently, the amount of recipe websites has increased significantly. These online recipes represent different cultures and cooking methods from various regions, and provide important indications on nutritional content. In recent years, the development of data science made data mining a popular research area. However, only a few researches in Taiwan have applied data mining in the studies of recipes and nutrients. Therefore, this work aims at utilizing machine learning models to discover health-related insights from recipes on social media. First, we collected over 15,000 Chinese recipes from the largest recipe website in Taiwan to build a recipe database. We then extracted information from this dataset through natural language processing methodologies so as to better understand the characteristics of various cuisines and ingredients. Thus, we can establish a classification model for the automatic categorization of recipes. We further performed cluster analysis for grouping nutrients to recognize the nutritional differences for each cluster and each cuisine type. The results showed that using the support vector machine (SVM) model can successfully classify recipes with an average F-score of 82%. We also analyzed the nutritional value of different cuisine categories and the possible health effects they may bring to the consumers. Our methods and findings can assist future work on extracting essential nutritional information from recipes and promoting healthier diets.

Highlights

Under the economic prosperity and the rapid development of science and technology, the social pattern has changed dramatically
We identify that the support vector machine (SVM) model performs best on the categorization of recipes into cuisine types
The findings in our experiments agree with previous research, in that dietary fiber is positively correlated with carbohydrates and protein, and negatively correlated with sugar

Summary

Introduction

Under the economic prosperity and the rapid development of science and technology, the social pattern has changed dramatically. The rise of social network has greatly changed the culinary culture. The choice of eating or cooking has become a hot topic, as well as the social influence of dietary choices [1]. The immediacy and convenience of the Internet have made sharing both food photos and cooking methods easier. The popularity of food search applications and food sharing websites is increasing [2]. Researchers have noticed the connection between recipes and diet habits under the effect of social networks [3]

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Conclusion