[Objective] This study constructs an evolution model for social sentiment analysis from the perspective of city profiles, aiming to grasp city dynamics, guide public opinions, as well as identify and predict potential issues. [Methods] We firstly used the LDA2Vec algorithm to extract city themes from each time window. Then, we applied a dictionary-based sentiment analysis method to fine-grain the emotion categories of city themes, and calculated their emotional intensities. Finally, we tracked city events arising changes of public sentiments with the TF-IDF algorithm, and built the ARMA model to predict social sentiment trends. [Results] Our model’s accuracy rate for predicting emotional intensity of “like” reached 97%, while those of the “dislike” scores were up to 90%. [Limitations] We did not include unexpected events as an influencing factor to the proposed model. [Conclusions] Our method could effectively identify city events and predict emotional changes of public opinions.
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