Interpretation of Social Media Data for Users Perception Assessment: Urban Planning Projects
Authors: Alexander A. Kharlamov, Maria Pilgun


Reviewer paper summaries: The paper discusses the development and testing of algorithms to analyze social media data using psycholinguistic methodologies and neural network technologies. The focus is on how users perceive specific situations, exemplified through an urban project in transportation system development. A dataset of over 20 million tokens was analyzed, revealing that user perceptions of the project were predominantly neutral. The study highlighted the integration of interdisciplinary approaches, combining computer science, linguistics, and psychology, to identify semantic and informational accents in user content, concluding that there is no significant social tension surrounding the project's implementation.
Summary of strengths: The topic is important and should be more thoroughly studied.
Summary of weaknesses: The paper is not well written, lacks clarity and has fundamental formatting errors.
Paper does not explain and presents the figures presented.
Simple explanations such as, how are tokens defined, what are NULL(s) and its composition, translation from other languages in the paper and so on. It is fairly hard to judge paper without proper definitions and explanations.
The paper needs significant rework, the topic could be interesting if studied thoroughly.
Comments, suggestions and typos: There are many fundamental formatting mistakes. Please refer to submission guides to format paper before submission.
Overall assessment scores: 1
Metareview score: 1
Metareview: The paper discusses the development and testing of algorithms to analyze social media data using psycholinguistic methodologies and neural network technologies. The focus is on how users perceive specific situations, exemplified through an urban project in transportation system development. A dataset of over 20 million tokens was analyzed, revealing that user perceptions of the project were predominantly neutral. The study highlighted the integration of interdisciplinary approaches, combining computer science, linguistics, and psychology, to identify semantic and informational accents in user content, concluding that there is no significant social tension surrounding the project's implementation.
Summary of reasons to publish: The topic is interesting.
A newly released dataset?