Abstract
Analysis of emotions has received recognition in urban studies as a mean to understand subjective quality of life. Availability of spontaneous user-generated online urban data generated by users in location based social networks broadens possibilities for such analysis as described in a number of studies. However the LBSN data is not shared deliberately by users and is not meant to be an expression of emotions, which makes its representativeness and validity questionable. Another source of data - public participation geo-information systems - helps to overcome these limitations however may have its own, such as a small and biased sample. In this paper the results of the comparative analysis of the distribution of emotions in St. Petersburg, Russia, visualized with LBSN and PPGIS data, are presented. The dataset is formed from user-generated comments on urban venues from Google Places and data from PPGIS platform Imprecity (www.imprecity.ru), where citizens deliberately share their emotions and comments about public spaces. The data samples contain 1800 emotional marks from Imprecity and 2450 geolocated comments from Google Places marked by experts and then processed with Naïve Bayes Classifier. Comparison of positive and negative emotional maps created for Imprecity and Google Places shows shared tendencies in emotional distribution, such as concentration of emotions in the city centre and collocation of positive and negative emotions. There are also differences in emotional distribution: PPGIS data shows local “emotional” islands, which correspond to pedestrian areas and green spaces. The comparative analysis appears to be insightful and capable of revealing recurring spatial tendencies in subjective perception of the city.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ellard, C.: Places of the Heart, The Psychogeography of Everyday Life. Bellevue Literary Press, New York (2015)
Gehl, J.: Cities for People. Island Press, Washington, Covelo, London (2010)
Whyte, W.: Social Life of Small Urban Spaces. Conservation Foundation, New York (1980)
Madden, K.: How to Turn a Place Around: A Handbook for Creating Successful Public Spaces. Project for Public Spaces, New York (2000)
Quercia, D., Schifanella, R., Aiello, L.: The shortest path to happiness: recommending beautiful, quiet, and happy routes in the city. In: Proceedings of the 25th ACM Conference on Hypertext and Social Media (HT 2014), pp. 116–125. ACM, New York (2014)
Ekman, P.: An argument for basic emotions. Cogn. Emot. 6(3), 169–200 (1992)
Elliott, C.: The affective reasoner: a process model of emotions in a multi-agent system. Ph.D. thesis, Institute for the Learning Sciences, Northwestern University, USA (1992)
Read, J.: Recognising affect in text using pointwise-mutual information. Ph.D. thesis, Department of Informatics, University of Sussex, England (2004)
Neviarouskaya, A., Prendinger, H., Ishizuka, M.: Compositionality principle in recognition of fine-grained emotions from text. In: Proceedings of the Proceedings of the Third International Conference on Weblogs and Social Media (ICWSM 2009), pp. 278–281. AAAI Press, Menlo Park (2009)
Alm, C., Roth, D., Sproat, S.: Emotions from text: machine learning for text-based emotion prediction. In: Proceedings of the Joint Conference on Human Language Technology. Empirical Methods in Natural Language Processing, Vancouver, Canada, pp. 579–586 (2005)
Brown, G., Kyttä, M.: Key issues and research priorities for public participation GIS (PPGIS): a synthesis based on empirical research. Appl. Geogr. 46, 126–136 (2014)
Martí, P., García-Mayor, C., Serrano-Estrada, L.: Identifying opportunity places for urban regeneration through LBSNs. Cities 90, 191–206 (2019)
Bollen, J., Mao, H., Pepe, A.: Modeling public mood and emotion: twitter sentiment and socio-economic phenomena. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, ICWSM 2011, Spain, pp. 450–453 (2011)
Gordon, J.: Comparative geospatial analysis of Twitter sentiment data during the 2008 and 2012 US Presidential elections. University of Oregon, USA (2013)
Antonelli, F.: City sensing: visualising mobile and social data about a city scale event. In: International Working Conference on Advanced Visual Interfaces, AVI 2014, Como, Italy, pp. 337–338. ACM (2014)
Balduini, M., Della Valle, E., Dell’Aglio, D., Tsytsarau, M., Palpanas, T., Confalonieri, C.: Social listening of city scale events using the streaming linked data framework. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8219, pp. 1–16. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41338-4_1
Bertrand, K.: Sentiment in New York city: a high resolution spatial and temporal view, USA (2013). arXiv preprint arXiv:1308.5010
Cho, E., Myers, S., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, pp. 1082–1090. ACM (2011)
Fujisaka, T., Lee, R., Sumiya, K.: Exploring urban characteristics using movement history of mass mobile micro bloggers, pp. 13–18. ACM, New York (2010)
Frias-Martinez, V.: Sensing urban land use with twitter activity. Telefonica Research, Madrid, Spain (2013)
Mitchell, L.: The geography of happiness: connecting twitter sentiment and expression, demographics, and objective characteristics of place. PloS One 5(8), 64–71 (2013)
Schweitzer, L.: Planning and social media: a case study of public transit and stigma on Twitter. J. Am. Plan. Assoc. 3(80), 218–238 (2014)
Hollander, J.: The new generation of public participation: internet-based participation tools. AU - Evans-Cowley Jennifer Plan. Pract. Res. 3(25), 397–408 (2010)
Value of Satisfaction. Habidatum report (2019). https://projects.habidatum.com/#value-of-satisfaction/. Accessed 19 Feb 2019
Grandi, R., Neri, F.: Sentiment analysis and city branding. In: Catania, B., et al. (eds.) New Trends in Databases and Information Systems. AISC, vol. 241, pp. 339–349. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-01863-8_36
Mohammad, S., Turney, P.: Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, California, USA, pp. 26–34 (2010)
Strapparava, C., Valitutti, A.: WordNet-Affect: an affective extension of WordNet. Part of the Lecture Notes in Computer Science, Italy. LNCS, vol. 3784, pp. 1083–1086 (2004)
Stone, P., Dunphy, D., Smith, M., Ogilvie, D.: The General Inquirer: A Computer Approach to Content Analysis. The MIT Press, Cambridge (1966)
WordNet-Affect. http://wndomains.fbk.eu/wnaffect.html. Accessed 19 Feb 2019
Kotelnikov, E., Klekovkina, M.: Avtomaticheskiy analiz tonal’nosti tekstov na osnove metodov mashinnogo obucheniya [Sentiment analysis of texts based on machine learning methods]. In: Proceedings of the Conference Dialog, Vyp. 11 (18), pp. 7–10. (2012). (In Russian) = E.B. Кoтeльникoв, M.B. Клeкoвкинa. Aвтoмaтичecкий aнaлиз тoнaльнocти тeкcтoв нa ocнoвe мeтoдoв мaшиннoгo oбyчeния. Кoмпьютepнaя лингвиcтикa и интeллeктyaльныe тexнoлoгии: Пo мaтepиaлaм eжeгoднoй Meждyнapoднoй кoнфepeнции « Диaлoг » . Bып. 11 (18). M.: Изд-вo PГГУ, c. 7–10. Mocквa, Poccия (2012)
Goldberg, Y., Levy, O.: Word2vec explained: deriving Mikolov et al.’s negative-sampling word-embedding method. Cornell University, Iceland (2014). arXiv preprint arXiv:1402.3722
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 1532–1543 (2014)
Bartunov, S.: Breaking sticks and ambiguities with adaptive skip-gram. In: Artificial Intelligence and Statistics, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, PMLR, Spain, vol. 51, pp. 130–138 (2016)
Poria, S.: Convolutional MKL based multimodal emotion recognition and sentiment analysis. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), Italy, pp. 439–448. IEEE (2016)
Chakrabarti, S.: Scalable feature selection, classification and signature generation for organizing large text databases into hierarchical topic taxonomies. VLDB J. 7(3), 163–178 (1998)
Lewis, D.: Method and apparatus for training a text classifier: Patent No. 5,675,710, 7 October 1997
Allmendinger, P.: Planning Theory, p. 239. Red Globe Press/Palgrave, New York (2002)
Friedmann, J.: Empowerment: The Politics of Alternative Development, p. 196. Blackwell, Cambridge (1992)
Healey, P.: Planning through debate: the communicative turn in planning theory. Town Plan. Rev. 2(63), 143 (1992)
Laurian, L.: Public participation in environmental decision making: findings from communities facing toxic waste cleanup. J. Am. Plan. Assoc. 1(70), 53–65 (2004)
Beierle, T., Thomas, C.: Democracy in Practice: Public Participation in Environmental Decisions. Routledge, Abingdon (2002)
Forrester, J.: The logistics of public participation in environmental assessment. Int. J. Environ. Pollut. 3(11), 316 (1999)
Kingston, R.: Public participation in local policy decision-making: the role of web-based mapping. Cartographic J. 2(44), 138–144 (2007)
Brown, G.: Public participation GIS (PPGIS) for regional and environmental planning: reflections on a decade of empirical research. J. Urban Reg. Inf. Syst. Assoc. 2(25), 12 (2012)
Brown, G.: Engaging the wisdom of crowds and public judgement for land use planning using public participation geographic information systems. Aust. Planner 3(52), 199–209 (2015)
Hasanzadeh, K., Laatikainen, T., Kyttä, M.: Place-based model of local activity spaces: individual place exposure and characteristics. J. Geograph. Syst. 20, 227–252 (2018)
Emotion Map. https://apkpocket.pw/emotion-map/edu.syr.ischool.orange.emotionmap.apk. Accessed 10 Feb 2019
Nold, C.: Bio mapping: how can we use emotion to articulate cities? Livingmaps Rev. (5) (2018)
Nielek, R., Ciastek, M., Kopeć, W.: Emotions make cities live. Towards mapping emotions of older adults on urban space, Germany (2017)
Nenko, A., Petrova, M.: Emotional geography of St. Petersburg: detecting emotional perception of the city space. In: Alexandrov, D.A., Boukhanovsky, A.V., Chugunov, A.V., Kabanov, Y., Koltsova, O. (eds.) DTGS 2018. CCIS, vol. 859, pp. 95–110. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02846-6_8
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Nenko, A., Petrova, M. (2019). Comparing PPGIS and LBSN Data to Measure Emotional Perception of the City. In: Alexandrov, D., Boukhanovsky, A., Chugunov, A., Kabanov, Y., Koltsova, O., Musabirov, I. (eds) Digital Transformation and Global Society. DTGS 2019. Communications in Computer and Information Science, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-37858-5_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-37858-5_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37857-8
Online ISBN: 978-3-030-37858-5
eBook Packages: Computer ScienceComputer Science (R0)