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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.

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100 st george st toronto on m5s 3g3

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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.

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100 st george st toronto on m5s 3g3

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Seed words are generated from official and crowdsourced food composition databases, and new food words are discovered by clustering food words within the word embedding space using the Gaussian mixture model. Or stay in touch by joining our alumni LinkedIn and alumni Facebook pages. This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Keywords: Food energy density; Food environment; Food words; Geographic information science; Health geography; Machine learning. George Campus Map. Facebook Twitter Instagram. Results: The approach successfully expands the food-related word dictionary and accurately predicts food energy density reaching Machine learning models are employed to predict the energy density classifications of these food words based on their feature vectors. 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. Abstract 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. Work where the world comes to think, discover and learn. 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. If you have any questions, please contact dean.

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Search form Search. This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Feeling Distressed? Seed words are generated from official and crowdsourced food composition databases, and new food words are discovered by clustering food words within the word embedding space using the Gaussian mixture model. To ensure a thorough exploration of the prediction problem, ten widely used machine learning models are evaluated. Facebook Twitter Instagram. 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. U of T Campus Updates including closures due to emergencies and weather conditions, and emergency contact information. Within the context, this study delves into the exploration of expanding food-related words along with their associated energy densities. 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. Request new password. Methods: This study addresses the aforementioned research gap by introducing a novel methodology for expanding the food-related word dictionary and predicting energy densities. George campus supporting our mission of research, teaching and scholarship.