Everyone included: Social impact of COVID-19, , 2020.
Wikipedia, COVID-19 pandemic, , 2021.
Domestic travel during the COVID-19 pandemic, , 2020.
Travelers prohibited from entry to the United States, , 2020.
K. Cohen, Tokyo 2020 Olympics officially postponed until 2021, , 2020.
Wikipedia, RNA virus, , 2021.
How does fake news of 5G and COVID-19 spread worldwide?, , 2021.
L. J. Chang, W. Li, L. Qin, W. J. Zhang, and S. Y. Yang, pSCAN: Fast and exact structural graph clustering, IEEE Trans. Knowl. Data Eng., vol. 29, no. 2, pp. 387-401, 2017.
R. El Bacha and T. T. Zin, Ranking of influential users based on user-tweet bipartite graph, in Proc. of 2018 IEEE Int. Conf. Service Operations and Logistics, and Informatics (SOLI), Singapore, 2018, pp. 97-101.
A. Rodríguez, C. Argueta, and Y. L. Chen, Automatic detection of hate speech on facebook using sentiment and emotion analysis, in Proc. of 2019 Int. Conf. Artificial Intelligence in Information and Communication (ICAIIC), Okinawa, Japan, 2019, pp. 169-174.
J. Zhou and C. Kwan, Missing link prediction in social networks, in Proc. 15th Int. Symp. Neural Networks, Minsk, Belarus, 2018, pp. 346-354.
A. Reyes-Menendez, J. R. Saura, and C. Alvarez-Alonso, Understanding #worldEnvironmentDay user opinions in twitter: A topic-based sentiment analysis approach, Int. J. Environ. Res. Public Health, vol. 15, no. 11, p. 2537, 2018.
C. H. Tan, L. L. Lee, J. Tang, L. Jiang, M. Zhou, and P. Li, User-level sentiment analysis incorporating social networks, in Proc. 17th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, New York, NY, USA, 2011, pp. 1397-1405.
A. Giachanou and F. Crestani, Like it or not: A survey of twitter sentiment analysis methods, ACM Comput. Surv., vol. 49, no. 2, p. 28, 2016.
R. R. Iyer, J. Chen, H. N. Sun, and K. Y. Xu, A heterogeneous graphical model to understand user-level sentiments in social media, arXiv preprint arXiv: 1912.07911, 2019.
H. B. Deng, J. W. Han, H. Li, H. Ji, H. N. Wang, and Y. Lu, Exploring and inferring user-user pseudo-friendship for sentiment analysis with heterogeneous networks, Stat. Anal. Data Min., vol. 7, no. 4, pp. 308-321, 2014.
C. A. Phillips, Multipartite graph algorithms for the analysis of heterogeneous data, PhD dissertation, Univ. Tennessee, Knoxville, TN, USA, 2015.
D. W. Zhou, S. Zhang, M. Y. Yildirim, S. Alcorn, H. H. Tong, H. Davulcu, and J. R. He, A local algorithm for structure-preserving graph cut, in Proc. 23rd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Halifax, Canada, 2017, pp. 655-664.
P. M. Comar, P. N. Tan, and A. K. Jain, A framework for joint community detection across multiple related networks, Neurocomputing, vol. 76, no. 1, pp. 93-104, 2012.
Y. Z. Sun, Y. T. Yu, and J. W. Han, Ranking-based clustering of heterogeneous information networks with star network schema, in Proc. 15th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Paris, France, 2009, pp. 797-806.
D. D. Lee and H. S. Seung, Algorithms for non-negative matrix factorization, in Proc. 13th Int. Conf. Neural Information Proc. Systems, Cambridge, MA, USA, 2001, pp. 535-541.
N. Gillis, The why and how of nonnegative matrix factorization, arXiv preprint arXiv: 1401.5226v2, 2014.
H. Abdi and L. J. Williams, Principal component analysis, WIRs Comput. Stat., vol. 2, no. 4, pp. 433-459, 2010.
M. E. Wall, A. Rechtsteiner, and L. M. Rocha, Singular value decomposition and principal component analysis, in A Practical Approach to Microarray Data Analysis, D. P. Berrar, W. Dubitzky, M. Granzow, eds. Norwell, MA, USA: Springer, 2003, pp. 91-109.
C. Ding, T. Li, W. Peng, and H. Park, Orthogonal nonnegative matrix t-factorizations for clustering, in Proc. 12th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Philadelphia, PA, USA, 2006, pp. 126-135.
D. Kim, S. Sra, and I. S. Dhillon, Fast newton-type methods for the least squares nonnegative matrix approximation problem, in Proc. 2007 SIAM Int. Conf. Data Mining, Minneapolis, MN, USA, 2007, pp. 343-354.
C. J. Lin, On the convergence of multiplicative update algorithms for nonnegative matrix factorization, IEEE Trans. Neural Netw., vol. 18, no. 6, pp. 1589-1596, 2007.
J. Kim and H. Park, Toward faster nonnegative matrix factorization: A new algorithm and comparisons, in Proc. of 2008 Eighth IEEE Int. Conf. Data Mining, Pisa, Italy, 2008, pp. 353-362.
F. Wang and P. Li, Efficient nonnegative matrix factorization with random projections, in Proc. 2010 SIAM Int. Conf. Data Mining, Columbus, OH, USA, 2010, pp. 281-292.
M. Annett and G. Kondrak, A comparison of sentiment analysis techniques: Polarizing movie blogs, in Proc. 21st Conference of the Canadian Society for Computational Studies of Intelligence, Windsor, Canada, 2008, pp. 25-35.
R. Hillmann and M. Trier, Sentiment polarization and balance among users in online social networks, , 2021.
M. Del Vicario, G. Vivaldo, A. Bessi, F. Zollo, A. Scala, G. Caldarelli, and W. Quattrociocchi, Echo chambers: Emotional contagion and group polarization on facebook, Sci. Rep., vol. 6, p. 37825, 2016.
S. M. Mohammad, X. D. Zhu, S. Kiritchenko, and J. Martin, Sentiment, emotion, purpose, and style in electoral tweets, Informat. Proc. Manag., vol. 51, no. 4, pp. 480-499, 2015.
K. Chakraborty, S. Bhattacharyya, R. Bag, and A. Hassanien, Sentiment analysis on a set of movie reviews using deep learning techniques, in Social Network Analytics Computational Research Methods and Techniques, Cambridge, MA, USA, 2019, pp. 127-147.
K. Sailunaz and R. Alhajj, Emotion and sentiment analysis from twitter text, J. Comput. Sci., vol. 36, p. 101003, 2019.
H. Meisheri, K. Ranjan, and L. Dey, Sentiment extraction from consumer-generated noisy short texts, in Proc. of 2017 IEEE Int. Conf. Data Mining Workshops (ICDMW), New Orleans, LA, USA, 2017, pp. 399-406.
A. S. M. Alharbi and E. de Doncker, Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information, Cogn. Syst. Res., vol. 54, pp. 50-61, 2019.
M. E. J. Newman, Modularity and community structure in networks, Proc. Natl. Acad. Sci. USA, vol. 103, no. 23, pp. 8577-8582, 2006.
M. Wang, C. K. Wang, J. X. Yu, and J. Zhang, Community detection in social networks: An in-depth benchmarking study with a procedure-oriented framework, Proc. VLDB Endow., vol. 8, no. 10, pp. 998-1009, 2015.
D. Cai, X. F. He, X. Y. Wu, and J. W. Han, Non-negative matrix factorization on manifold, in Proc. 2008 8th IEEE Int. Conf. Data Mining, Pisa, Italy, 2008, pp. 63-72.
H. Wang, F. P. Nie, H. Huang, and F. Makedon, Fast nonnegative matrix tri-factorization for large-scale data co-clustering, in Proc. 22nd Int. Joint Conf. Artificial Intelligence, Barcelona, Spain, 2011, pp. 1553-1558.
TextBlob: Simplified text processing, , 2020.
C. H. Q. Ding, T. Li, and M. I. Jordan, Convex and semi-nonnegative matrix factorizations, IEEE Trans. Patt. Anal. Mach. Intell., vol. 32, no. 1, pp. 45-55, 2010.
H. Abe and H. Yadohisa, Orthogonal nonnegative matrix tri-factorization based on tweedie distributions, Adv. Data Anal. Classi., vol. 13, no. 4, pp. 825-853, 2019.
P. K. Shivaswamy and T. Jebara, Permutation invariant SVMs, in Proc. 23rd Int. Conf. Machine Learning, Pittsburgh, PA, USA, 2006, pp. 817-824.