Extraction of hidden topics in urban context based on the
Internet publications analysis

D 2022

Extraction of hidden topics in urban context based on the Internet publications analysis

TIKHONOVA, Olga, Aleksandr KHRULKOV, Aleksandr ANTONOV, Stanislav SOBOLEVSKY, Sergey A. MITYAGIN et. al.

Základní údaje

Originální název

Extraction of hidden topics in urban context based on the Internet publications analysis

Autoři

TIKHONOVA, Olga, Aleksandr KHRULKOV, Aleksandr ANTONOV, Stanislav SOBOLEVSKY (112 Bělorusko, domácí) a Sergey A. MITYAGIN

Vydání

Amsterdam, Procedia Computer Science, od s. 23-33, 11 s. 2022

Nakladatel

Elsevier

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10100 1.1 Mathematics

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Odkazy

URL

Kód RIV

RIV/00216224:14310/22:00130437

Organizační jednotka

Přírodovědecká fakulta

ISSN

DOI

http://dx.doi.org/10.1016/j.procs.2022.10.204

Klíčová slova anglicky

named entities recognition; Natural language processing; texts clustering; topic modeling; urban environment; urban environment object

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 22. 1. 2024 09:48, Mgr. Marie Šípková, DiS.

Anotace

V originále

The problem considered in the article is the systematic lack of data on the objects of the urban environment for management and decision-making. This problem is particularly acute in the lack of data on points of attraction, informal and thematic places of interest. At the same time, this kind of information is necessary for the qualitative development of the urban environment. This article discusses an approach to creating new information resources based on the analysis of publications and messages of citizens, which can be used to effectively manage the development of the city and improve the quality of the urban environment. For example, to create new centers of attraction for citizens and tourists and effective landscaping. Currently, there is a public demand for the semantic content of the urban environment, considering historical and cultural associations, informal symbols. Traditionally, this request is met through surveys of the population in urban improvement projects. This article presents an approach to supplementing such surveys with information from Internet social networks processed by natural language analysis methods to extract hidden topics and thematic objects of the urban environment. The approach is demonstrated based on the example of the city of St. Petersburg in the Russian Federation.

Citovat

TIKHONOVA, Olga, Aleksandr KHRULKOV, Aleksandr ANTONOV, Stanislav SOBOLEVSKY a Sergey A. MITYAGIN. Extraction of hidden topics in urban context based on the Internet publications analysis. In Boukhanovsky A., Krzhizhanovskaya V., Klimova A. Procedia Computer Science. Amsterdam: Elsevier, 2022, s. 23-33. ISSN 1877-0509. Dostupné z: https://dx.doi.org/10.1016/j.procs.2022.10.204.

@inproceedings{2267007,
   author = {Tikhonova, Olga and Khrulkov, Aleksandr and Antonov, Aleksandr and Sobolevsky, Stanislav and Mityagin, Sergey A.},
   address = {Amsterdam},
   booktitle = {Procedia Computer Science},
   doi = {http://dx.doi.org/10.1016/j.procs.2022.10.204},
   editor = {Boukhanovsky A., Krzhizhanovskaya V., Klimova A.},
   keywords = {named entities recognition; Natural language processing; texts clustering; topic modeling; urban environment; urban environment object},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Amsterdam},
   note = {Bude zaindexováno na WoS. Doplnit a poté vykázat do RIV.},
   pages = {23-33},
   publisher = {Elsevier},
   title = {Extraction of hidden topics in urban context based on the Internet publications analysis},
   url = {https://doi.org/10.1016/j.procs.2022.10.204},
   year = {2022}
}

TY  - JOUR
ID  - 2267007
AU  - Tikhonova, Olga - Khrulkov, Aleksandr - Antonov, Aleksandr - Sobolevsky, Stanislav - Mityagin, Sergey A.
PY  - 2022
TI  - Extraction of hidden topics in urban context based on the Internet publications analysis
PB  - Elsevier
CY  - Amsterdam
N1  - Bude zaindexováno na WoS. Doplnit a poté vykázat do RIV.
KW  - named entities recognition
KW  - Natural language processing
KW  - texts clustering
KW  - topic modeling
KW  - urban environment
KW  - urban environment object
UR  - https://doi.org/10.1016/j.procs.2022.10.204
N2  - The problem considered in the article is the systematic lack of data on the objects of the urban environment for management and decision-making. This problem is particularly acute in the lack of data on points of attraction, informal and thematic places of interest. At the same time, this kind of information is necessary for the qualitative development of the urban environment. This article discusses an approach to creating new information resources based on the analysis of publications and messages of citizens, which can be used to effectively manage the development of the city and improve the quality of the urban environment. For example, to create new centers of attraction for citizens and tourists and effective landscaping. Currently, there is a public demand for the semantic content of the urban environment, considering historical and cultural associations, informal symbols. Traditionally, this request is met through surveys of the population in urban improvement projects. This article presents an approach to supplementing such surveys with information from Internet social networks processed by natural language analysis methods to extract hidden topics and thematic objects of the urban environment. The approach is demonstrated based on the example of the city of St. Petersburg in the Russian Federation.
ER  -

TIKHONOVA, Olga, Aleksandr KHRULKOV, Aleksandr ANTONOV, Stanislav SOBOLEVSKY a Sergey A. MITYAGIN. Extraction of hidden topics in urban context based on the Internet publications analysis. In Boukhanovsky A., Krzhizhanovskaya V., Klimova A. \textit{Procedia Computer Science}. Amsterdam: Elsevier, 2022, s.~23-33. ISSN~1877-0509. Dostupné z: https://dx.doi.org/10.1016/j.procs.2022.10.204.

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