這是一個社群網絡時代，主要依靠社群互動和資訊流來完成任何領域的任何事業。社群互動是日常和永久現象的基礎，必須從不同方面加以理解，以便為進步和生產力提供更聰明且有效的解決方案。在本研究中，我們從兩個觀點：內容和主題，分析社群互動的特徵和特點，使用不同的案例研究，並提供大規模的多維覆蓋。內容分析是針對文本材料的結構、關係，以及談話互動之探索的細粒度分析；而主題分析是針對通訊資料的模式研究，著重於整體觀點。我們的目標是對社群互動進行深入分析，並從不同互動形式的集體觀點進行研究。

對於第一種觀點，基於內容的分析，我們關注於以文本資訊做為主要研究來源的內容。微博的文本被認為充滿了情感和情緒。我們透過實現確定性的分類方法，結合多維度的正負性及喚醒度空間，對文本進行定量情緒預測。這引發了分類準確性和標籤不一致等挑戰。本研究試圖透過提出一種基於文本的情緒識別機率模型，名為 TERMS，來克服上述限制。該模型能夠同時降低確定性情緒分類器的錯誤率，以及由標註者所造成的評級分歧之影響。

對於第二種觀點，主題分析，我們對世界各國的社群互動資料（節點度，呼叫量和頻率）進行實驗，並分析了人口對它們的影響。人口增長又稱城市化是一個日益嚴重的問題，它引發了城市的變化，舉凡經濟、基礎設施、社會互動等屬性。研究這些變化需要以科學為基礎來進行理解。我們透過配置一種名為聯繫網路的網路，並且運用冪次規則來分析密切社群互動中的上述變化。在世界各地的不同城市當中，我們記錄了人口對社群互動的影響，並且以綜合和個人等不同層面來呈現。

因此，為此目的，在主題和內容分析中進行的案例研究都強調了社群互動的特徵，並且以不同規模的環境及參數來探索社群互動，以表明社群互動用以達成任何目標的本質。

Cities are evolving microstructure that offers a wide range of opportunities, exposure and chances to flourish. The opportunities offered by cities attract majority of people towards them resulting in urbanization. Urbanization is the process of population concentration and increasing densities in urban areas. Population concentration in urban areas places unprecedented pressure on city authorities for predictive and quantitative solutions for sustainable city infrastructures. The increase in population triggers changes in city attributes such as demographics, economy, housing, health, communication, networks, etc. that affect city dwellers' quality of life. It is essential to understand these variations or shifts in city attributes for informed urban planning. These variations in city attributes relative to population are explicable by scaling laws. Urban scaling laws quantify the properties of cities as a function of their population size.

City attributes are regulated by information flow, connection, and communication that results in accelerated social and economic activities. The primary determinant that catalyzes information flow and communication in cities is social interaction. It is the phenomenon that predominantly underlies major diverse city services, economies, and infrastructure, which makes it essential to be analyzed and estimated through urban scaling properties. Social interaction follows scale-invariant superlinear with population size, asserting an increase in human interaction based on city expansion. However, it is not yet known if this is the case for social interaction among close contacts, that is, whether population growth influences connectivity in a close circle of social contacts that are dynamic, distinctive, and short-spanned. Following this, a network is configured, named contact networks, based on familiarity. We study the urban scaling property for three social connectivity parameters (degree, call frequency, and call volume) and analyze it at the collective level and the individual level for various cities around the world. The results show superlinear scaling of close-contact interactions based on population; however, the increase in level of connectivity is minimal relative to the general scenario. The statistical distributions exhibit the impact of city size on close individual interactions. Knowledge of the estimated variations in social interaction relative to urbanization can help urban planners to cultivate a clear approach to infrastructure development, devising sustainable economic policies, and improving individuals’ social and personal lives that are struggling with social exclusion and isolation in bigger cities.
Social interaction is an enriched entity that can be explored further to have a thorough understanding of the population impact on city dwellers' cognitive experiences. Social interaction is embedded with plenteous emotional content that exhibits emotional states, moods, and perspectives. Emotional states are inherently social and it is likely that population also influences emotional experiences, as emotions are regulated by social activity. To analyze emotional states in social interaction, that is, emotion interaction, urban scaling properties are applied to the Twitter activity and emotion interactions of major cities in the USA and UK. To our knowledge, emotion interaction has yet to be explored through urban scaling laws. Furthermore, the world is facing a deadly pandemic, which has severely affected the physical and emotional well-being of humans. This study compares emotion interaction during the pandemic and before it started to analyze the impact of population on deviating emotional states. The findings suggest that emotion interaction follows superlinear scaling, i.e., there is an increase in emotion interaction with an increase in population. However, negative emotion interaction tends to increase more in response to population. The statistics on emotional interaction in cities reflect cognitive experiences of cities as well as an understanding of human behavior in expanding urban environments, which can be useful in defining the narratives of cities and developing citizen-centric sustainable and resilient city plans.

Emotion interaction is reflective of emotional states and affective experiences, but the emotions embedded in the interactions are multifaceted. Assigning a discrete label to an interaction or a text is insufficient; therefore, this study recognizes emotions in the dimensions of valence and arousal on a multidimensional space for the interactions on microblogs. Microblogs generate a vast amount of data in which users express their emotions regarding almost all aspects of everyday life. Capturing affective content from these context-dependent and subjective texts is a challenging task. To address this, we propose an intelligent probabilistic model for textual emotion recognition in multidimensional space (TERMS) that captures the subjective emotional boundaries and contextual information embedded in a text for robust emotion recognition. TERMS is driven by an emotion classifier that takes context-aware emotion patterns from the linguistic and contextual information and learns the parameters of the Gaussian mixture model (GMM) from the valence and arousal ratings provided by annotators for each text to generate the emotion distributions in order to cover the varying emotional perceptions. TERMS jointly exploit the proposed context-aware emotion classifier and the learned GMM parameters for accurate emotion recognition. Our large-scale simulations on annotated data show that compared to baseline and state-of-the-art models, TERMS achieved better performance in terms of distinguishability, prediction, and classification performance. The transparent learning process of TERMS makes it easily adaptable and interpretable for future extensions and real-world applications. In addition, TERMS provide insights on emotion classes, the annotation patterns, and the models application in different scenarios.

This dissertation comprehensively discusses the social and emotion interaction through urban scaling laws and estimates the shifts in these attributes with respect to population. The variational shifts demonstrate the narrative of the cities and is applicable in designing and planning sustainable city infrastructures. This study further provides measures to understand and recognize emotional dimensions by proposing an intelligent model TERMS that learns from varying perspectives and aims to achieve the subjectivity and individual emotional boundaries hidden in texts. The objective of this dissertation is to analyze and study different dimensions of social and emotional phenomena as it directly affects the well-being and quality of life at all levels, both collective and individual.

[1] Second-level administrative country subdivisions. http://www.fao.
org/geonetwork/srv/en/metadata.show?id=12691&currTab=
simple.
[2] Whoscall. https://whoscall.com/en-US/.
[3] World urbanization prospects: The 2018 revision-highlights.
https://www.un.org/development/desa/publications/
2018-revision-of-world-urbanization-prospects.html,2018.
[4] M. Abdul-Mageed and L. Ungar. Emonet: Fine-grained emotion detection with gated recurrent neural networks. In Proc. of Association for Computational Linguistics, pages 718—-728, 2017.
[5] M. Abdullah and M. Hadzikadic. Sentiment analysis of twitter data: Emotions revealed regarding Donald Trump during the 2015-16 primary debates. In IEEE Proc. of ICTAI, pages 760–764, 2017.
[6] N. Alvarez-Gonzalez, A. Kaltenbrunner, and V. G ́omez. Uncovering the limits of text-based emotion detection. arXiv preprint arXiv:2109.01900, 2021.
[7] L. G. Alves, H. V. Ribeiro, and R. S. Mendes. Scaling laws in the dynam-
ics of crime growth rate. Physica A: Statistical Mechanics and its Applications,
392(11):2672–2679, 2013.
[8] K. Anand, S. Karade, S. Sen, and R. Gupta. Sars-cov-2: camazotz’s curse. Medical Journal, Armed Forces India, 76(2):136, 2020.
[9] S. Arbesman, J. M. Kleinberg, and S. H. Strogatz. Superlinear scaling for innovation in cities. Physical Review E, 79(1):016115, 2009.
[10] E. Arcaute, E. Hatna, P. Ferguson, H. Youn, A. Johansson, and M. Batty. Con-
structing cities, deconstructing scaling laws. Journal of Royal Society Interface,
12, 2015.
[11] R. Arthur and H. T. Williams. Scaling laws in geo-located twitter data. PloS one, 14(7):e0218454, 2019.
[12] P. Basile, V. Basile, M. Nissim, N. Novielli, V. Patti, et al. Sentiment analysis of
microblogging data., Jan. 2018.
[13] M. Batty. The size, scale, and shape of cities. science, 319(5864):769–771, 2008.
[14] M. Batty. The new science of cities. Mit Press, 2013.
[15] M. Batty. A theory of city size. Science, 340(6139):1418–1419, 2013.
[16] E. A. Bauer, A. L. Braitman, M. R. Judah, and K. P. Cigularov. Worry as a mediator between psychosocial stressors and emotional sequelae: Moderation by contrast avoidance. Journal of affective disorders, 266:456–464, 2020.
[17] R. F. Baumeister and M. R. Leary. The need to belong: desire for interpersonal attachments as a fundamental human motivation. Psychological bulletin, 117(3):497, 1995.
[18] C. Baziotis, N. Athanasiou, A. Chronopoulou, A. Kolovou, G. Paraskevopoulos, N. Ellinas, S. Narayanan, and A. Potamianos. Ntua-slp at semeval-2018 Task 1:Predicting affective content in tweets with deep attentive rnns and transfer learning.arXiv preprint arXiv:1804.06658, 2018.
[19] A. Benavoli, G. Corani, J. Demˇsar, and M. Zaffalon. Time for a change: a tutorial for comparing multiple classifiers through bayesian analysis. The Journal of Machine Learning Research, 18(1):2653–2688, 2017.
[20] A. Bermingham and A. Smeaton. Classifying sentiment in microblogs:is brevityan advantage? In Int’l Conf. Information and Knowledge Management, pages 1833–1836, 2010.
[21] B. J. Berry. Urbanization. In Urban ecology, pages 25–48. Springer, 2008.
[22] A. Betella and P. F. Verschure. The affective slider: A digital self-assessment scale for the measurement of human emotions. PloS One, 11(2):e0148037, 2016.
[23] L. Bettencourt, J. Lobo, and H. Youn. The hypothesis of urban scaling: formalization, implications and challenges. arXiv preprint arXiv:1301.5919, 2013.
[24] L. Bettencourt and G. West. A unified theory of urban living. Nature,
467(7318):912–913, 2010.
[25] L. M. Bettencourt. The origins of scaling in cities. science, 340(6139):1438–1441,2013.
[26] L. M. Bettencourt and J. Lobo. Urban scaling in europe. Journal of The Royal Society Interface, 13(116):20160005, 2016.
[27] L. M. Bettencourt, J. Lobo, D. Helbing, C. K ̈uhnert, and G. B. West. Growth,
innovation, scaling, and the pace of life in cities. Proceedings of the national
academy of sciences, 104(17):7301–7306, 2007.
[28] L. M. Bettencourt, J. Lobo, D. Strumsky, and G. B. West. Urban scaling and its deviations: Revealing the structure of wealth, innovation and crime across cities. PloS one, 5(11):e13541, 2010.
[29] L. M. Bettencourt, J. Lobo, and G. B. West. Why are large cities faster? universal scaling and self-similarity in urban organization and dynamics. The European Physical Journal B, 63(3):285–293, 2008.
[30] L. M. A. Bettencourt. The origins of scaling in cities. Science 340,
340(6139):1438–1441, June 2013.
[31] L. M. A. Bettencourt, J. Lobo, D. Helbing, C. Kuhnert, and G. B. West. Growth innovation, scaling, and the pace of life in cities. Proceedings of the National Academy of Sciences of the USA, 104(17):7301–7306, 2007.
[32] C. M. Bishop. Pattern Recognition and Machine Learning. Springer-Verlag, New York, 2006.
[33] V. D. Blondel, A. Decuyper, and G. Krings. A survey of results on mobile phone datasets analysis. EPJ Data Science, 4(10), 2015.
[34] S. K. Brooks, R. K. Webster, L. E. Smith, L. Woodland, S. Wessely, N. Greenberg, and G. J. Rubin. The psychological impact of quarantine and how to reduce it: rapid review of the evidence. The lancet, 395(10227):912–920, 2020.
[35] K. B ̈uchel and M. von Ehrlich. Cities and the structure of social interactions: Evidence from mobile phone data. 2017.
[36] S. Buechel and U. Hahn. Emobank: Studying the impact of annotation perspective and representation format on dimensional emotion analysis. In Proc. of EACL (Short Papers), pages 578–585, Apr. 2017.
[37] S. Buechel and U. Hahn. Emotion analysis as a regression problem—dimensional models and their implications on emotion representation and metrical evaluation. In ACM Proc. of ECAI, pages 1114–1122, Aug. 2016.
[38] A. Burns. Emotion and urban experience: Implications for design. Design Issues, 16(3):67–79, 2000.
[39] R. A. Calvo and S. Mac Kim. Emotions in text: dimensional and categorical models. Comput. Intell., 29(3):527–543, Aug. 2013.
[40] R. A. Calvo, D. N. Milne, M. S. Hussain, and H. Christensen. Natural language processing in mental health applications using non-clinical texts. Nat. Lang. Eng., 23(5):649–685, 2017.
[41] J. Carrillo-de Albornoz, J. Rodr ́ıguez Vidal, and L. Plaza. Feature engineering for sentiment analysis in e-health forums. PLoS One, 13(11):e0207996, 2018.
[42] U. Census Bureau. Geography program, incorporated place, September 2019.
[43] S. Charlot and G. Duranton. Communication externalities in cities. Journal of Urban Economics, 56(3):581–613, 2004.
[44] Y.-Y. Cheng, Y.-M. Chen, W.-C. Yeh, and Y.-C. Chang. Valence and arousal-
infused bi-directional lstm for sentiment analysis of government social media management. Applied Sciences, 11(2):880, 2021.
[45] C. Cottineau, E. Hatna, E. Arcaute, and M. Batty. Paradoxical interpretations of urban scaling laws. arXiv preprint arXiv:1507.07878, 2015.
[46] C. Cottineau, E. Hatna, and M. Arcaute, E.and Batty. Paradoxical interpretations of urban scaling laws. arXiv:1507.07878., 2015.
[47] T. Cox, J. Houdmont, and A. Griffiths. Rail passenger crowding, stress, health and safety in britain. Transportation Research Part A: Policy and Practice, 40(3):244– 258, 2006.
[48] P. M. de Medeiros Carvalho, M. M. Moreira, M. N. A. de Oliveira, J. M. M.
Landim, and M. L. R. Neto. The psychiatric impact of the novel coronavirus out-
break. Psychiatry research, 286:112902, 2020.
[49] A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1):1–22, 1977.
[50] D. Demszky, D. Movshovitz-Attias, J. Ko, A. Cowen, G. Nemade, and
S. Ravi. Goemotions: A dataset of fine-grained emotions. arXiv preprint
arXiv:2005.00547, 2020.
[51] P. Deville, C. Linard, S. Martine, M. Gilbert, F. Stevensf, A. Gaughanf, V. D. Blondela, and A. J. Tatem. Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences of the USA, 111(45):15888–15893,2014.
[52] L. Dini, A. Bittar, C. Robin, F. Segond, and M. Montaner. Soma: The smart socialcustomer relationship management tool: Handling semantic variability of emotion analysis with hybrid technologies. In Sentiment Analysis in Social Networks, pages 197–209. 2017.
[53] V. Duppada, R. Jain, and S. Hiray. Seernet at semeval-2018 Task 1: Domain adaptation for affect in tweets. arXiv preprint arXiv:1804.06137, Apr. 2018.
[54] P. Ekman, E. R. Sorenson, and W. V. Friesen. Pan-cultural elements in facial displays of emotion. Science, 164(3875):86–88, 1969.
[55] A. Engelniederhammer, G. Papastefanou, and L. Xiang. Crowding density in urban environment and its effects on emotional responding of pedestrians: Using wearable device technology with sensors capturing proximity and psychophysiological
emotion responses while walking in the street. Journal of Human Behavior in the Social Environment, 29(5):630–646, 2019.
[56] B. Felbo, A. Mislove, A. Søgaard, I. Rahwan, and S. Lehmann. Using millions
of emoji occurrences to learn any-domain representations for detecting sentiment,
emotion and sarcasm. arXiv preprint arXiv:1708.00524, Aug. 2017.
[57] C. S. Fischer, R. K. Merton, and R. K. Merton. The urban experience. Harcourt Brace Jovanovich New York, 1976.
[58] R. Florida. Cities and the creative class. Routledge, 2005.
[59] L. Gallegos, K. Lerman, A. Huang, and D. Garcia. Geography of emotion: Where in a city are people happier? In Proceedings of the 25th International Conference Companion on World Wide Web, pages 569–574, 2016.
[60] Y. Ghafoor, F. H. Calderon, L. S.-W. Chen, and Y.-S. Chen. Emotion interactio
in cities. In 2021 IEEE 22nd International Conference on Information Reuse and
Integration for Data Science (IRI), pages 91–98. IEEE, 2021.
[61] Y. Ghafoor, Y.-S. Chen, and K.-T. Chen. Social interaction scaling for contact
networks. Sustainability, 11(9):2545, 2019.
[62] B. Ghanem, D. Buscaldi, and P. Rosso. Textrolls: Identifying russian trolls on
twitter from a textual perspective. arXiv preprint arXiv:1910.01340, 2019.
[63] A. Giachanou and F. Crestani. Like it or not: A survey of twitter sentiment analysis methods. ACM Comput. Surv., 49(2):1–41, Jun. 2016.
[64] E. Glaeser. Cities, productivity, and quality of life. Science, 333(6042):592–594, 2011.
[65] E. L. Glaeser. Learning in cities. Journal of urban Economics, 46(2):254–277,
1999.
[66] J. Guan. Proving personality-related differences in valence and arousal annotation in social media tasks, 2017.
[67] S. Hamidi, S. Sabouri, and R. Ewing. Does density aggravate the covid-19 pandemic? early findings and lessons for planners. Journal of the American Planning Association, 86(4):495–509, 2020.
[68] M. Hasan, E. Rundensteiner, and E. Agu. Emotex: Detecting emotions in twitter
messages. In Academy of Science and Engineering (ASE), 2014.
[69] M. Hasan, E. Rundensteiner, and E. Agu. Automatic emotion detection in text streams by analyzing twitter data. Intl. J. of Data Science and Analytics, 7(1):35-51, 2018.
[70] C. Herrera-Yag ̈ue, C. M. Schneider, T. Couronn ́e, Z. Smoreda, R. M. Benito, P. J. Zufiria, and M. C. Gonzalez. The anatomy of urban social networks and its implications in the searchability problem. Scientific reports, 5:10265, 2015.
[71] J. R. Hershey and P. A. Olsen. Approximating the kullback leibler divergence between gaussian mixture models. In IEEE Int’l Conf. Acoustics, Speech and Signal Processing, pages IV–317, 2007.
[72] J. Holt-Lunstad, T. B. Smith, and J. B. Layton. Social relationships and mortality risk: a meta-analytic review. PLoS medicine, 7(7):e1000316, 2010.
[73] J. Hsu. Population density does not doom cities to pandemic dangers. Scientific American, 16, 2020.
[74] Z. Huang and J. Epps. Detecting the instant of emotion change from speech using a martingale framework. In IEEE Proc. of ICASSP, pages 5195–5199, Mar. 2016.
[75] J. Jacobs. The Death and Life of Great American Cities. Random House: New York, 1961.
[76] H. Kawamichi, S. K. Sugawara, Y. H. Hamano, K. Makita, T. Kochiyama, and
N. Sadato. Increased frequency of social interaction is associated with enjoyment enhancement and reward system activation. Scientific reports, 6:24561, 2016.
[77] M. J. Keeling and P. Rohani. Modeling infectious diseases in humans and animals. Princeton University Press, 2011.
[78] J. Kim and D. R. Fesenmaier. Measuring emotions in real time: Implications for tourism experience design. Journal of Travel Research, 54(4):419–429, 2015.
[79] J. K. Kruschke. Tutorial: Bayesian data analysis. In CogSci, 2015.
[80] T. Kynk ̈a ̈anniemi, T. Karras, S. Laine, J. Lehtinen, and T. Aila. Improved precision and recall metric for assessing generative models. In Proc. of NIPS, pages 3927–3936, 2019.
[81] M. Li. Application of sentence-level text analysis: The role of emotion in an
experimental learning intervention. Journal of Experimental Social Psychology,
99:104278, 2022.
[82] R. Li, L. Dong, J. Zhang, X. Wang, W.-X. Wang, Z. Di, and H. E. Stanley. Simple spatial scaling rules behind complex cities. Nature communications, 8(1):1841,2017.
[83] B. Liu. Sentiment analysis and subjectivity. Handbook of natural language processing, 2(2010):627–666, Feb. 2010.
[84] J. Lobo, L. M. Bettencourt, D. Strumsky, and G. B. West. Urban scaling and the production function for cities. PLoS One, 8(3):e58407, 2013.
[85] N. Lykousas, C. Patsakis, A. Kaltenbrunner, and V. G ́omez. Sharing emotions at scale: The vent dataset. In Proceedings of the International AAAI Conference on Web and Social Media, volume 13, pages 611–619, 2019.
[86] A. Malko, C. Paris, A. Duenser, M. Kangas, D. Moll ́a, R. Sparks, and S. Wan.
Demonstrating the reliability of self-annotated emotion data. In Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, pages 45–54, 2021.
[87] K. V. Mardia. Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3):519–530, 1970.
[88] R. Meo and E. Sulis. Processing affect in social media: A comparison of methods
to distinguish emotions in tweets. ACM T. Internet Techn., 17(1):1–25, Jan. 2017.
[89] G. Miritello, R. Lara, M. Cebrian, and E. Moro. Limited communication capacity unveils strategies for human interaction. Scientific reports, 3:1950, 2013.
[90] A. Mishra and S. Vishwakarma. Analysis of tf-idf model and its variant for document retrieval. In 2015 international conference on computational intelligence and communication networks (cicn), pages 772–776. IEEE, 2015.
[91] A. Mislove, S. Lehmann, Y.-Y. Ahn, J.-P. Onnela, and J. Rosenquist. Understanding the demographics of twitter users. In Proceedings of the International AAAI Conference on Web and Social Media, volume 5, 2011.
[92] S. Mohammad, F. Bravo-Marquez, M. Salameh, and S. Kiritchenko. Semeval-2018 Task 1: Affect in tweets. In ACL Proc. SemEval, pages 1–17, Jun. 2018.
[93] S. M. Mohammad. Challenges in sentiment analysis. In A practical guide to
sentiment analysis, pages 61–83. Springer, 2017.
[94] S. M. Mohammad. Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 english words. In Proc. 56th Annual Meeting Association for Computational Linguistics, pages 174–184, 2018.
[95] S. M. Mohammad. Sentiment analysis: Automatically detecting valence, emotions, and other affectual states from text. In Emotion Measurement, pages 323–379. Elsevier, 2021.
[96] S. M. Mohammad. Sentiment analysis: Detecting valence, emotions, and other affectual states from text. In Emotion measurement, pages 201–237. Elsevier, Jan. 2016.
[97] S. M. Mohammad and F. Bravo-Marquez. WASSA-2017 shared task on emotion intensity. arXiv preprint arXiv:1708.03700, Aug. 2017.
[98] S. M. Mohammad and F. Bravo-Marquez. Emotion intensities in tweets. arXivpreprint arXiv:1708.03696, Aug. 2017.
[99] K. Mulcrone. Detecting emotion in text. UMM CSci Senior Seminar, Apr. 2012.
[100] R. Nielek, M. Ciastek, and W. Kope ́c. Emotions make cities live: towards mapping emotions of older adults on urban space. In Proceedings of the International Conference on Web Intelligence, pages 1076–1079, 2017.
[101] C. Nold et al. Emotional cartography. Bio Mapping website, 2009.
[102] M. Oliveira, C. Bastos-Filho, and R. Menezes. The scaling of crime concentration in cities. PloS one, 12(8):e0183110, 2017.
[103] A. J. O’malley, S. Arbesman, D. M. Steiger, J. H. Fowler, and N. A. Christakis. Egocentric social network structure, health, and pro-social behaviors in a national panel study of americans. PloS one, 7(5):e36250, 2012.
[104] G. Paltoglou and M. Thelwall. Seeing stars of valence and arousal in blog posts.IEEE Trans. Affect. Comput., 4(1):116–123, Nov. 2013.
[105] W. Pan, G. Ghoshal, C. Krumme, M. Cebrian, and A. Pentland. Urban characteristics attributable to density-driven tie formation. Nature communications, 4:1961,2013.
[106] S. Park, J. Kim, S. Ye, J. Jeon, H. Y. Park, and A. Oh. Dimensional emotion
detection from categorical emotion. In Proceedings of the 2021 Conference on
Empirical Methods in Natural Language Processing, pages 4367–4380, 2021.
[107] B. Parkinson. Emotions are social. British journal of psychology, 87(4):663–683,1996.
[108] J. Peen, R. A. Schoevers, A. T. Beekman, and J. Dekker. The current status of urban-rural differences in psychiatric disorders. Acta Psychiatrica Scandinavica,121(2):84–93, 2010.
[109] M. d. C. P ́erez-Fuentes, M. D. M. Molero Jurado, ́A. Martos Mart ́ınez, and J. J. G ́azquez Linares. Threat of covid-19 and emotional state during quarantine: Positive and negative affect as mediators in a cross-sectional study of the spanish population. PloS one, 15(6):e0235305, 2020.
[110] I. Perikos and I. Hatzilygeroudis. A framework for analyzing big social data and modelling emotions in social media. In IEEE Proc. of BigDataService, pages 80– 84, 2018.
[111] R. Plutchik. The nature of emotions human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice.
American Scientist, 89(4):344–350, 2001.
[112] D. Preotiuc-Pietro, H. A. Schwartz, G. Park, J. C. Eichstaedt, M. Kern, L. Ungar,
and E. P. Shulman. Modelling valence and arousal in facebook posts. In ACL Proc. of WASSA, pages 9–15, Jun. 2016.
[113] J. Pribil, A. Pribilova, and J. Matousek. Artefact determination by GMM-based continuous detection of emotional changes in synthetic speech. In IEEE Proc. of TSP, pages 45–48, Jul. 2019.
[114] D. Pumain, F. Paulus, C. Vacchiani-Marcuzzo, and J. Lobo. An evolutionary theory for interpreting urban scaling laws. Cybergeo: European Journal of Geography,
2006.
[115] T. Rawat and S. Jain. A dimensional representation of depressive text. In Data Analytics nd Management, pages 175–187. Springer, 2021.
[116] B. Resch, A. Summa, G. Sagl, P. Zeile, and J.-P. Exner. Urban emotions—geo-semantic emotion extraction from technical sensors, human sensors and crowd-sourced data. In Progress in location-based services 2014, pages 199–212.Springer, 2015.
[117] B. Resch, A. Summa, P. Zeile, and M. Strube. Citizen-centric urban planning through extracting emotion information from twitter in an interdisciplinary spacetime-linguistics algorithm. Urban Planning, 1(2):114–127, 2016.
[118] F. L. Ribeiro, J. Meirelles, F. F. Ferreira, and C. R. Neto. A model of urban scaling laws based on distance dependent interactions. Royal Society open science,4(3):160926, 2017.
[119] B. S. Rintyarna, R. Sarno, and C. Fatichah. Enhancing the performance of sentiment analysis task on product reviews by handling both local and global context.Int. J. Inform Decis. Sci., 12(1):75–101, 2020.
[120] G. Rose. Feminism & geography: The limits of geographical knowledge. U of Minnesota Press, 1993.
[121] S. Rosenthal, N. Farra, and P. Nakov. Semeval-2017 Task 4: Sentiment analysis in twitter. In ACL Proc. of SemEval-2017, pages 502–518, Aug. 2017.
[122] J. A. Russell. A circumplex model of affect. J. Personality and Social Psychology,39(6):1161–1178, 1980.
[123] D. Rybski, E. Arcaute, and M. Batty. Urban scaling laws, 2019.
[124] D. Rybski, S. V. Buldyrev, S. Havlin, F. Liljeros, and H. A. Makse. Scaling laws
of human interaction activity. Proceedings of the National Academy of Sciences, 106(31):12640–12645, 2009.
[125] H. Sadr, M. M. Pedram, and M. Teshnehlab. Multi-view deep network: A deep model based on learning features from heterogeneous neural networks for sentiment analysis. IEEE Access, 8:86984–86997, 2020.
[126] J. Saram ̈aki, E. A. Leicht, E. L ́opez, S. G. Roberts, F. Reed-Tsochas, and R. I.
Dunbar. Persistence of social signatures in human communication. Proceedings of
the National Academy of Sciences, 111(3):942–947, 2014.
[127] J. Saram ̈aki, E. A. Leicht, E. L ́opez, S. G. B. Roberts, F. Reed-Tsochas, and R. I. M.Dunbar. Persistence of social signatures in human communication. Proceedings of
the National Academy of Sciences, 111(3):942–947, 2014.
[128] E. Saravia, C. Argueta, and Y.-S. Chen. Unsupervised graph-based pattern extraction for multilingual emotion classification. Social Network Analysis and Mining,6(1):1–21, 2016.
[129] E. Saravia, H.-C. T. Liu, Y.-H. Huang, J. Wu, and Y.-S. Chen. Carer: Contextu-
alized affect representations for emotion recognition. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3687–3697, 2018.
[130] M. Schlapfer, L. Bettencourt, S. Grauwin, M. Raschke, R. Claxton, Z. Smoreda, G. West, and C. Ratti. The scaling of human interactions with city size. jr soc. Interface, 11(98):20130789–20130789, 2014.
[131] M. Seshadri, S. Machiraju, A. Sridharan, J. Bolot, C. Faloutsos, and J. Leskove. Mobile call graphs: beyond power-law and lognormal distributions. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 596–604, 2008.
[132] A. Seyeditabari, N. Tabari, and W. Zadrozny. Emotion detection in text: A review. arXiv preprint arXiv:1806.00674v1, Jun. 2018.
[133] X. Song, J. Petrak, and A. Roberts. A deep neural network sentence level classification method with context information. arXiv preprint arXiv:1809.00934, 2018.
[134] E. Strano and V. Sood. Rich and poor cities in europe. an urban scaling approach to mapping the european economic transition. PloS one, 11(8):e0159465, 2016.
[135] C. Strapparava and R. Mihalcea. Learning to identify emotions in text. In ACM Proc. of SAC, pages 1556–1560, Mar. 2008.
[136] K. Sun, J. Yu, Y. Huang, and X. Hu. An improved valence-arousal emotion space for video affective content representation and recognition. In Multimedia and Expo ICME 2009. Int’l Conf. on IEEE, pages 566–569, 2009.
[137] J. Suttles and N. Ide. Distant supervision for emotion classification with discrete binary values. In Int’l Conf. Intelligent Text Processing and Computational Linguistics, pages 121—-136, 2013.
[138] L. Sveikauskas. The productivity of cities. The Quarterly Journal of Economics, 89(3):393–413, 1975.
[139] S. Symeonidis, D. Effrosynidis, and A. Arampatzis. A comparative evaluation of pre-processing techniques and their interactions for twitter sentiment analysis. Expert Syst. Appl., 110:298–310, Nov. 2018.
[140] G. Tavares, S. Mastelini, et al. User classification on online social networks by post frequency. In Anais Principais do XIII Simp ́osio Brasileiro de Sistemas de
Informac ̧ ̃ao, pages 464–471, 2017.
[141] M. Tizzoni, K. Sun, D. Benusiglio, M. Karsai, and N. Perra. The scaling of human contacts in reaction-diffusion processes on heterogeneous metapopulation net-
works. arXiv preprint arXiv:1411.7310, 2014.
[142] M. Tizzoni, K. Sun, D. Benusiglio, M. Karsai, and N. Perra. The scaling of human contacts and epidemic processes in metapopulation networks. Scientific reports,5:15111, 2015.
[143] E. P. Torres, E. A. Torres, M. Hern ́andez- ́Alvarez, and S. G. Yoo. Emotion reconition related to stock trading using machine learning algorithms with feature selection. IEEE Access, 8:199719–199732, 2020.
[144] I. Trabelsi, D. B. Ayed, and N. Ellouze. Evaluation of influence of arousal-valence primitives on speech emotion recognition. Int. Arab. J. Inf. Technol., 15(4):756–
762, Jul. 2018.
[145] G. A. van Kleef, A. Cheshin, A. H. Fischer, and I. K. Schneider. The social nature of emotions. Frontiers in Psychology, 7:896, 2016.
[146] B. Vinayagasundaram, R. Mallik, M. Aravind, R. Aarthi, and S. Senthilrhaj. Building a generative model for affective content of music. In IEEE Proc. of ICRTIT,pages 1–6, Apr. 2016.
[147] C. Wang, R. Pan, X. Wan, Y. Tan, L. Xu, C. S. Ho, and R. C. Ho. Immediate
psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (covid-19) epidemic among the general population in china. International journal of environmental research and public health, 17(5):1729,
2020.
[148] J.-C. Wang, Y.-H. Yang, H.-M. Wang, and S.-K. Jeng. Modeling the affective content of music with a gaussian mixture model. IEEE Trans. Affective Computing,
6(1):56–68, 2015.
[149] A. B. Warriner, V. Kuperman, and M. Brysbaert. Norms of valence, arousal, and dominance for 13,915 english lemmas. Behavior Research Methods, 45(4):1191–
1207, 2013.
[150] L. Wirth. Urbanism as a way of life. American journal of sociology, 44(1):1–24,1938.
[151] C. Wu, F. Wu, S. Wu, Z. Yuan, J. Liu, and Y. Huang. Semi-supervised dimen-
sional sentiment analysis with variational autoencoder. Knowledge-Based Systems, 165:30–39, 2019.
[152] Y.-H. Yang and H. H. Chen. Prediction of the distribution of perceived music emotions using discrete samples. IEEE T. Audio Spe., 19(7):2184–2196, Feb. 2011.
[153] Y.-H. Yang and J.-Y. Liu. Quantitative study of music listening behavior in a social and affective context. IEEE Trans. Multimedia, 15(6):1304–1315, 2013.
[154] P. Zeile, B. Resch, J.-P. Exner, and G. Sagl. Urban emotions: benefits and risks in using human sensory assessment for the extraction of contextual emotion information in urban planning. In Planning support systems and smart cities, pages
209–225. Springer, 2015.
[155] S. Zhang, X. Xu, Y. Pang, and J. Han. Multi-layer attention based cnn for target-dependent sentiment classification. Neural processing letters, 51(3):2089–2103, 2020.
[156] S. Zhao, H. Yao, and X. Jiang. Predicting continuous probability distribution of image emotions in valence-arousal space. In Proc. 23rd Annual ACM Conf. on Multimedia, pages 879–882, 2015.
[157] S. Zhu, S. Li, and G. Zhou. Adversarial attention modeling for multi-dimensional emotion regression. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 471–480, 2019.