راد، رؤیا، موسوی، محمد، و وردی، فاطمه (1399). استفاده از یادگیری عمیق در تشخیص خودکار بیماری گیاهان براساس پردازش تصویر برگ. تحقیقات سامانه‌ها و مکانیزاسیون کشاورزی، 21(76)، ۴۹-۶۸.
راستگو، راضیه، و کیانی، کوروش (1398). شناسایی چهره با استفاده از تنظیم دقیق شبکه‌های کانولوشنی عمیق و رویکرد یادگیری انتقالی. مجله مدل‌سازی در مهندسی، 17 (58)، ۱۰۳-۱۱۱.
زاهدی حقیقی، سیده سعیده، سخایی، سید محمود، و دلیری، محمدرضا (1398). تشخیص حالت‌های احساسی مبتنی‌بر EEG با استفاده از شبکه یادگیری عمیق. مجله مهندسی پزشکی زیستی، 13 (2)، ۹۵-۱۰۴.
فولادی، صابر، فرسی، حسن، و محمدزاده، سجاد (1399). تشخیص و طبقه‌بندی سرطان پوست با استفاده از یادگیری عمیق. مجله علمی دانشگاه علوم پزشکی بیرجند، 26 (1)، ۴۴-۵۳.
موسوی، سیدمهدی، عبادی، حمید، و کیانی، عباس (1398). ارائه روشی بهینه مبتنی‌بر یادگیری عمیق به‌منظور طبقه‌بندی طیفی مکانی تصاویر با قدرت تفکیک مکانی بالا در مناطق نیمه‌شهری. نشریه علوم و فنون نقشه‌برداری، 9 (2)، 170-151.
وب‌سایت خانه بیگ دیتای ایران (1397). پردازش متن با Jhazm نسخه جاوا کتابخانه هضم برای پردازش زبان فارسی. بازیابی شده در 28 بهمن 1401، از https://bigdata-ir.com
یوسفی متقاعد، مریم، و صبوحی، هادی (1398). مروری بر تحلیل احساسات شبکه‌های اجتماعی درحوزه قطبیت. کنفرانس بین‌المللی وب‌پژوهی، 3 و 5 اردیبهشت ماه، تهران، ایران.
Baly, R., Hobeica, R., Hajj, H., El-Hajj, W., Shaban, K.B., & Al-Sallab, A. (2016). A Meta-Framework for Modeling the Human Reading Process in Sentiment Analysis. ACM Transactions on Information Systems (TOIS), 35(1), 1-21. https://doi.org/10.1145/2950050.
Benevenuto, F., Araújo, M., & Ribeiro, F. (2015). Sentiment Analysis Methods for Social Media. In Proceedings of Brazilian Symposium on Multimedia and the Web (WebMedia). ACM, Manaus, Brazil, 11–11. https: //doi.org/10.1145/2820426.2820642.
Bonasoli de Oliveira, W., Dorini, L.B., Minetto, R., & Silva, T.H. (2020). OutdooeSent: Setiment analysis of urban outdoor images by using semantic and deep features. ACM transaction on information systems, 1(1), 1-29.
Campos, V., Jou, B., & Giró i Nieto, X. (2017). From pixels to sentiment: Fine-tuning CNNs for visual sentiment prediction. Image and Vision Computing, 65, 15 -22. https://doi.org/10.1016/j.imavis.2017.01.011.
Chapman, L., Resch, B., Sadler, J., Zimmer, S., Roberts, H., & Petutsching, A. (2018). Investigating the emotional responses of individuals to urban green space using Twitter data: a critical comparison of tree different methods of sentiment analysis. Urban Planning, 3 (1), 21-33.
Chen, F., Gao, Y., Cao, D., & Ji, R. (2015). Multimodal hypergraph learning for microblog sentiment prediction. In IEEE International Conference on Multimedia and Expo (ICME). IEEE, Turin, Italy, 1–6. https://doi.org/10.1109/ICME.2015.7177477.
Chen, NC., Nagakura, T., & Larson, K. (2016). Social media as Complementary Tool to Evaluate Cities -Data Mining Innovation Districts in Boston, Herneoja, Aulikki; Toni Österlund & Piia Markkanen (eds.), Complexity & Simplicity - Proceedings of the 34th eCAADe Conference - Volume 2, University of Oulu, Oulu, Finland, 22-26 August 2016, 447-456.
Chen, T. (2014). Deep SentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks. CoRR abs/1410.8586, 1–7. arXiv:1410.8586. http://arxiv.org/abs/1410.8586.
Chen, T., Xu, R., He, Y., & Wang, X. (2017). Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Systems with Applications, 72, 221-230.
Dhall, A., Goecke, R., & Gedeon, T. (2015). Automatic Group Happiness Intensity Analysis. IEEE Transactions on Affective Computing, 6(1), 13–26. https://doi.org/10.1109/TAFFC.2015.2397456.
Eboli, L., & Mazzulla, G. (2011). A methodology for evaluating transit service quality based on subjective and objective measures from the passenger’s point of view. Transport Policy, 18(1), 172–181. http://dx.doi.org/10.1016/j.tranpol.2010.07.007.
EURISY. (2017). Good City Life: crowdsourcing satellite data and emotions to map our urban landscape. Retrieved 2022, Feb. 18, from https://goodcitylife.org/
Fathullah, A., & S.Willis, K. (2018). Engaging the Senses: The Potential of Emotional Data for Participation in Urban Planning. Urban Science, 2(4), 1-21. doi:10.3390/urbansci2040098.
He, W., Zha, S., & Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33, 464–472.
Hollander, J. B., & Renski, H. (2015). Measuring urban attitudes using Twitter: an exploratory study. Lincoln Institute of Land Policy.
Huang, H., Gartner, G., Klettner, S., & Schmidt, M. (2014). Considering affective responses towards environments for enhancing location based services. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 93-96. doi:10.5194/isprsarchives-XL-4-93-2014.
Hutto, C.J., & Gilbert, E. (2014). VADER: A parsimonious rule-based model foe sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216-225. https://doi.org/10.1609/icwsm.v8i1.14550.
Ji, R., Cao, D., & Lin, D. (2015). Cross-Modality Sentiment Analysis for Social Multimedia. IEEE International Conference on Multimedia Big Data.
Kenyon, S., & Lyons, G. (2003). The value of integrated multimodal traveler information and its potential contribution to modal change. Transport Research Part F: Traffic Psychology Behavior, 6(1), 1–21. http://dx.doi.org/10.1016/s1369-8478(02)00035-9.
Kim, E., Rosenwasser, D., & Castillo Lopez, J.L.G. (2020). Urban emotion: The interrogation of social media and its implication within urban context. Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September, 475-482.
Liu, B., & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. Mining Text Data, 415-463.
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis lectures on human language technologies. 1-167.
Liu, L., Silva, E.A., Wu, C., & Wang, H. (2017). A machine learning-based method for the large-scale evaluation of qualities of the urban environment. Computers, environment and urban systems, 65, 113-125.
Mazzulla, G., & Forciniti, C. (2012). Spatial association techniques for analyzing trip distribution in an urban area. European Transport Research Review, 4(4), 217–233.http://dx.doi.org/10.1007/s12544-012-0082-9.
Oteros-Rozas, E., Martin-Lpez, B., Fagerholm, N., Bieling, C., & Plieninger, T. (2018). Using social media photos to explore the relation between cultural ecosystem services and landscape features across five European sites. Ecol. Indic. 94, 74–86.
Parrett, G. (2016). 3.5 million photos shared every minute in 2016. Deloitte. https://goo.gl/uwF81P.
Poria, S., Peng, H., Hussain, A., Howard, N., & Cambria, E. (2017). Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis. Neurocomputing, 261, 217-230.
Resch, B., Summa, A., Sagl, G., Zeile, P., & Exner, J. (2014). Urban emotions-Geo-semantic emotion extraction from technical sensors, human sensors and crowd sourced data. In G. Gartner & H. Huang (Eds.), Progress in location-based service, Springer.
Ribeiro, F.N., Araujo, M., Goncalves, P., Goncalves, M.A., & Benevenuto, F. (2016). SentiBench -A benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Science, 5(1), 1–29. https://doi.org/10.1140/epjds/s13688-016-0085-1.
Rossi, L., Boscaro, E., & Torsello, A. (2018). Venice through the Lens of Instagram: A Visual Narrative of Tourism in Venice, 18 Companion of the Web Conference, 1190-1197.
Schwartz, A.J., Dodds, P.S., O`Neil-Dunne, J.P.M., Danforth, C.M., & Ricketts, T. (2019). Visitors to urban greenspace have higher sentiment and lower negativity on Twitter. People and Nature, 476-485.
Sdoukopoulos, A., Nikolaidou, A., Pitsiava-Latinopoulou, M., & Papaioannou, P. (2018). Use of social media for assessing sustainable urban mobility indicators. International Journal of sustainable development and planning, 13(2), 338-348.
Setiawan, E.I., Juwiantho, H., Santoso, J., Sumpeno, S., Fujisawa, K., & Purnomo, M.H. (2021). Multiview sentiment analysis with Image-Text-Concept features of Indonesian social media posts, International Journal of Intelligent Engineering & Systems, 14(2), 521-535.
Silva, T.H., Vaz de Melo, P.O.S., Almedia, J.M., Salles, J., & Loureiro, A.A.F. (2013). A comparison of Foursquare and Instagram to the study of city dynamics and urban social behavior, Proc. ACM SIGKDD Int. Workshop on Urban Computing (UrbComp’13).
Sim, J., Miller, P., & Swarup, S. (2020). Tweeting the high line life: a social media lens on urban green spaces. Sustainability, 12, 1-18.
Soleymani, M., Garcia, D., Jou, B., Schuller, B., Chang, S., & Pantic, M. (2017). A survey of multimodal sentiment analysis. Image and Vision Computing, 65, 3–14.
Tebyanian, N. (2020). Application of machine learning for urban landscape design: a primer for landscape architect. Journal of digital landscape architecture, 5, 217-226.
Tieskens, K.F., Zantenm B, T.V., Schulp, C.J.E., & Verburg, P.H. (2018). Aesthetic appreciation of the cultural landscape through social media: An analysis of revealed preference in the Dutch river landscape. Landscape and Urban Planning, 177, 128–137.
Tu, W., Cao, J., Yue, Y., Shaw, S., Zhou, M., Wang, Z., Chang, X., Xu, Y., & Li, Q. (2017). Coupling mobile phone and social media data: A new approach to understanding urban functions and diurnal patterns. International Journal of Geographical Information Science, 31, 2331–2358.
Wu, J., Lin, Z., & Zha, H. (2016). Multi-view common space learning for emotion recognition in the wild. In ACM International Conference on Multimodal Interaction. ACM, Tokyo, Japan, 464–471. https://doi.org/10.1145/2993148.2997631.
You, L., & Tuncer, B. (2016). Exploring public sentiments for livable places based on a crowd-calibrated sentiment analysis mechanism. IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM). August 18-21, USA: San Francisco.
Zhan, X., Wang, Y., Rao, Y., & Li, Q. (2019). Learning from Multi-annotator Data: A Noise-aware Classification Framework. ACM Transactions on Information Systems (TOIS), 37(2), 1 -28. https://doi.org/10.1145/3309543.
Zhou, G-Y & Huang, J. X. (2017). Modeling and Mining Domain Shared Knowledge for Sentiment Analysis.
ACM Transactions on Information Systems (TOIS), 36(2), 1-36.
https://doi.org/10.1145/3091995.
)