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Background: The exponential growth of location-based social media LBSM data has ushered in novel prospects for investigating the urban food environment in health geography research. However, previous studies have primarily relied on word dictionaries with a limited number of food words and employed common-sense categorizations to determine the healthiness of those words. To enhance the analysis of the urban food environment using LBSM data, it is 100 st george st toronto on m5s 3g3 to develop a more comprehensive list of food-related words. Within the context, this study delves into the exploration of expanding food-related words along with their associated energy densities.

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100 st george st toronto on m5s 3g3

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Keywords: Food energy density; Food environment; Food words; Geographic information science; Health geography; Machine learning. Donate Online.

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The University of Toronto , founded in , is one of the oldest universities in Canada and one of the world's leading research centres. The scientific study of psychology has a long and illustrious history at the University of Toronto, beginning with J. Mark Baldwin's founding of the tenth psychology laboratory in North America in Psychology gained department status at the University's St. George campus in , and was later expanded with the addition of the Scarborough and Mississauga campuses. The three campuses offer independent undergraduate programs but a common, integrated graduate program. Presently, there are more than full-time and cross-appointed faculty at the three campuses, with many of the latter based at affiliated hospitals and research institutes. The combined expertise of our faculty spans all major areas of psychology and reflects a wide range of approaches to the study of mind, brain, and behaviour.

100 st george st toronto on m5s 3g3

George Campus Map. Office of the Faculty Registrar. Stay connected, learn more and get involved alumni artsci. U of T Campus Updates including closures due to emergencies and weather conditions, and emergency contact information.

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However, previous studies have primarily relied on word dictionaries with a limited number of food words and employed common-sense categorizations to determine the healthiness of those words. This method makes a valuable contribution to building a more comprehensive list of food words that can be used in geography and public health studies by mining geotagged social media data. Conclusions: This study proposes a novel method to expand food-related vocabulary and predict the food energy density based on machine learning and word embedding. Request new password. Campus Status U of T Campus Updates including closures due to emergencies and weather conditions, and emergency contact information. Work where the world comes to think, discover and learn. Facebook Twitter Instagram. Results: The approach successfully expands the food-related word dictionary and accurately predicts food energy density reaching If you have any questions, please contact dean. U of T Campus Updates including closures due to emergencies and weather conditions, and emergency contact information. Machine learning models are employed to predict the energy density classifications of these food words based on their feature vectors. To enhance the analysis of the urban food environment using LBSM data, it is crucial to develop a more comprehensive list of food-related words. Background: The exponential growth of location-based social media LBSM data has ushered in novel prospects for investigating the urban food environment in health geography research. This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.

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Results: The approach successfully expands the food-related word dictionary and accurately predicts food energy density reaching George Campus Map. Background: The exponential growth of location-based social media LBSM data has ushered in novel prospects for investigating the urban food environment in health geography research. To ensure a thorough exploration of the prediction problem, ten widely used machine learning models are evaluated. Find Us. However, previous studies have primarily relied on word dictionaries with a limited number of food words and employed common-sense categorizations to determine the healthiness of those words. Keywords: Food energy density; Food environment; Food words; Geographic information science; Health geography; Machine learning. This method makes a valuable contribution to building a more comprehensive list of food words that can be used in geography and public health studies by mining geotagged social media data. By giving to the University of Toronto, you can support groundbreaking research and teaching, help shape our next generation of leaders and address the social issues you care about most. Search form Search.