Treffer: تحلیل احساسات کاربران پارکهای شهری مبتنی بر داده های فضای مجازی؛ با بهره گیری از روشهای مدل گرا و غیر مدل گرا مطالعه موردی پارک ملت تهران.

Among the types of urban spaces, urban green spaces and parks, as city breathing spaces, are lush and relaxing areas that have been selected as the case of this research paper. Therefore, this article aims to analyze the emotions of users of Mellat Park in Tehran in the form of analytical research based on a quantitative method (supervised machine learning approach and lexical-based. After preprocessing and labeling, the data were examined and analyzed using two methods, such as model-oriented and non-modeloriented. Emotions were also examined and analyzed using the Python programming language. The comparison of these two methods revealed that among the machine learning algorithms, XGBoost has the highest accuracy at 87%, while K-nearest neighbors and support vector machines have lower accuracy but are still capable of predicting emotions in green spaces. The lexical method (using the VADER dictionary) has a lower predictive ability compared to machine learning. Finally, the stacking ensemble learning model, which was used to increase the accuracy of the model, has the ability to predict emotions based on the results of the confusion matrix (96%). Therefore, using the method based on virtual space data, it is possible to predict the emotions of users of other urban green spaces with high speed and accuracy in Tehran. [ABSTRACT FROM AUTHOR]

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