Fetching the paper…
Reading the bibliography…
The article describes the approaches for forming different predictive features of tweet data sets and using them in the predictive analysis for decision-making support.
Fruchterman, T.M., Reingold, E.M.: Graph drawing by force-directed placement. Software: Practice and experience 21
1991
Earlier work this paper cites.
Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proc. 20th int. conf. very large data bases, VLDB. vol. 1215, pp. 487–499 (1994)
1994
Earlier work this paper cites.
Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., Verkamo, A.I.: Finding interesting rules from large sets of discovered association rules. In: Proceedings of the third international conference on Information and knowledge management. pp. 401–407 (1994)
1994
Earlier work this paper cites.
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I., et al.: Fast discovery of association rules. Advances in knowledge discovery and data mining 12
1996
Earlier work this paper cites.
Brin, S., Motwani, R., Silverstein, C.: Beyond market baskets: Generalizing association rules to correlations. In: Proceedings of the 1997 ACM SIGMOD international conference on Management of data. pp. 265–276 (1997)
1997
Earlier work this paper cites.
Srikant, R., Vu, Q., Agrawal, R.: Mining association rules with item constraints. In: Kdd. vol. 97, pp. 67–73 (1997)
1997
Earlier work this paper cites.
Sutton, R.S., Barto, A.G., et al.: Introduction to reinforcement learning, vol. 2. MIT press Cambridge (1998)
1998
Earlier work this paper cites.
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: International Conference on Database Theory. pp. 398–416. Springer (1999)
1999
Earlier work this paper cites.
Gouda, K., Zaki, M.J.: Efficiently mining maximal frequent itemsets. In: Proceedings 2001 IEEE International Conference on Data Mining. pp. 163–170. IEEE (2001)
2001
Earlier work this paper cites.
Pons, P., Latapy, M.: Computing communities in large networks using random walks. In: International symposium on computer and information sciences. pp. 284–293. Springer (2005)
2005
Earlier work this paper cites.
Csardi, G., Nepusz, T., et al.: The igraph software package for complex network research. InterJournal, complex systems 1695
2006
Earlier work this paper cites.
Chui, C.K., Kao, B., Hung, E.: Mining frequent itemsets from uncertain data. In: Pacific-Asia Conference on knowledge discovery and data mining. pp. 47–58. Springer (2007)
2007
Earlier work this paper cites.
Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis. pp. 56–65 (2007)
2007
Cited alongside, same era.
Benevenuto, F., Rodrigues, T., Cha, M., Almeida, V.: Characterizing user behavior in online social networks. In: Proceedings of the 9th ACM SIGCOMM conference on Internet measurement. pp. 49–62 (2009)
2009
Cited alongside, same era.
Asur, S., Huberman, B.A.: Predicting the future with social media. In: 2010 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology. vol. 1, pp. 492–499. IEEE (2010)
2010
Cited alongside, same era.
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.: Measuring user influence in twitter: The million follower fallacy. In: Proceedings of the International AAAI Conference on Web and Social Media. vol. 4 (2010)
2010
Cited alongside, same era.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. Nature 518
2015
Later among the works it cites.
Mahmud, J.: IBM Watson Personality Insights: The science behind the service. Tech. rep., Technical report, IBM (2016)
2016
Later among the works it cites.
Carpenter, B., Gelman, A., Hoffman, M.D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., Riddell, A.: Stan: A probabilistic programming language. Journal of statistical software 76
2017
Later among the works it cites.
Pavlyshenko, B.M.: Forecasting of Events by Tweets Data Mining. Electronics and information technologies (10), 71–85 (2018)
2018
Later among the works it cites.
Pavlyshenko, B.M.: Can Twitter Predict Royal Baby’s Name ? Electronics and information technologies (11), 52–60 (2019)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th international conference on World wide web. pp. 591–600 (2010)
2010
Cited alongside, same era.
Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREc. vol. 10, pp. 1320–1326 (2010)
2010
Cited alongside, same era.
Shamma, D., Kennedy, L., Churchill, E.: Tweetgeist: Can the twitter timeline reveal the structure of broadcast events. CSCW Horizons pp. 589–593 (2010)
2010
Cited alongside, same era.
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. Journal of computational science 2
2011
Cited alongside, same era.
Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D.B.: Bayesian data analysis. Chapman and Hall/CRC (2013)
2013
Cited alongside, same era.
2013
Cited alongside, same era.
Kruschke, J.: Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press (2014)
2014
Cited alongside, same era.
2019
Later among the works it cites.
Balakrishnan, V., Khan, S., Arabnia, H.R.: Improving cyberbullying detection using twitter users’ psychological features and machine learning. Computers & Security 90
2020
Later among the works it cites.
Kraaijeveld, O., De Smedt, J.: The predictive power of public twitter sentiment for forecasting cryptocurrency prices. Journal of International Financial Markets, Institutions and Money 65
2020
Later among the works it cites.
Pavlyshenko, B.M.: Modeling COVID-19 Spread and Its Impact on Stock Market Using Different Types of Data. Electronics and information technologies (14), 3–21 (2020)
2020
Later among the works it cites.
Pavlyshenko, B.: Bayesian Regression Approach for Building and Stacking Predictive Models in Time Series Analytics. In: International Conference on Data Stream Mining and Processing. pp. 486–500. Springer (2020)
2020
Later among the works it cites.
Pavlyshenko, B.M.: Sales Time Series Analytics Using Deep Q-learning. International Journal of Computing 19
2020
Later among the works it cites.
Wang, M., Hu, G.: A novel method for twitter sentiment analysis based on attentional-graph neural network. Information 11
2020
Later among the works it cites.