Social Networks: Analysis of Evolution and Sentiment
| Thesis title in Czech: | Sociální sítě: analýza vývoje a sentimentu |
|---|---|
| Thesis title in English: | Social Networks: Analysis of Evolution and Sentiment |
| Key words: | dobývání znalostí|sociální sítě|detekce významných uzlů|analýza sentimentu|strojové učení |
| English key words: | data mining|social networks|detection of influential individuals|sentiment analysis|machine learning |
| Academic year of topic announcement: | 2021/2022 |
| Thesis type: | Bachelor's thesis |
| Thesis language: | angličtina |
| Department: | Department of Theoretical Computer Science and Mathematical Logic (32-KTIML) |
| Supervisor: | doc. RNDr. Iveta Mrázová, CSc. |
| Author: | Bc. Samuel Fanči - assigned and confirmed by the Study Dept. |
| Date of registration: | 30.05.2022 |
| Date of assignment: | 30.05.2022 |
| Confirmed by Study dept. on: | 20.06.2022 |
| Date and time of defence: | 29.06.2023 09:00 |
| Date of electronic submission: | 10.05.2023 |
| Date of submission of printed version: | 10.05.2023 |
| Date of proceeded defence: | 29.06.2023 |
| Opponents: | Mgr. Marta Vomlelová, Ph.D. |
| Guidelines |
| The student shall review the following topics in his bachelor thesis:
- a survey of known paradigms relevant to evolution in social networks and the detection of influential individuals (PageRank, HITS, betweenness, etc.), - recapitulation and mutual comparison of machine learning techniques applicable to sentiment analysis in (dynamic) social networks - e.g., Bayesian classifiers, MLP networks, SVM machines, decision trees, random forests, recurrent neural networks, and their variants like the LSTM network model. The student will focus on some of these topics in more detail. Further, he shall propose a suitable strategy for finding influential individuals and analyzing sentiment in real-world social networks - e.g., the Enron Email Data Set (https://www.cs.cmu.edu/~enron/) or the data obtained from Reddit and Twitter posts relevant to GME (GameStop Corporation). He shall implement the models (application of the NetworkX library is assumed). Evaluating the obtained results and experience gained while working on the above-mentioned tasks will form an important part of the thesis. |
| References |
| 1. Některé z dostupných základních učebnic, resp. přehledových článků vhodných pro zvolené téma, např.:
- Ch. C. Aggarwal: Data Mining: The Textbook, Springer, (2015). - B. Liu: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Springer, (2007). - B. Liu: Sentiment Analysis,Cambridge University Press, (2015). 2. Články, resp. kapitoly z knih: - C. C. Aggarwal: Social Network Analysis (Chapter 19) from C. C. Aggarwal: Data Mining: The Textbook, Springer, (2015), https://link.springer.com/chapter/10.1007%2F978-3-319-14142-8_19 - A.-L. Barabasi: The Scale-Free Property (Chapter 4) from A.-L. Barabasi, M. Posfai: Network Science, Cambridge University Press, (2016), pp. 321-376. - T. Lappas, K. Liu, and E. Terzi: A Survey of Algorithms and Systems for Expert Location in Social Networks (Chapter 8) from C. D. Aggarwal (Ed.): Social Network Data Analytics, Springer, (2011), https://link.springer.com/content/pdf/10.1007%2F978-1-4419-8462-3_8.pdf - J. Leskovec, J. Kleinberg, and Ch. Faloutsos: Graph Evolution: Densification and Shrinking Diameters, in: ACM Transactions on Knowledge Discovery from Data, Vol. 1, No. 1, Article 2, (2007), http://doi.acm.org/10.1145/1217299.1217301, 41 p. - B. Liu: Opinion Mining and Sentiment Analysis (Chapter 11) from B. Liu: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, 2nd Ed., Springer, (2011), http://sirius.cs.put.poznan.pl/~inf89721/Seminarium/Web_Data_Mining__2nd_Edition__Exploring_Hyperlinks__Contents__and_Usage_Data.pdf - L. Molm, D. Schaefer, and J. Collett: The Value of Reciprocity, in: Social Psychology Quarterly, Vol. 70, (2007), pp. 199 - 217. - K. Sechidis, G. Tsoumakas, and I. Vlahavas: On the Stratification of Multi-label Data, in: LNAI 6913, (2011), pp. 145 - 158. - J. Sun and J. Tang: A Survey of Models and Algorithms for Social Influence Analysis (Chapter 7) from C. D. Aggarwal (Ed.): Social Network Data Analytics, Springer, (2011), https://link.springer.com/content/pdf/10.1007%2F978-1-4419-8462-3_7.pdf - T. Zhang, T. Zhu, J. Li, M. Han, W. Zhou, and P. S. Yu: Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination, in: Journal of LATEX Class Files, Vol. 14, No. 8, (2015), 12 p. 3. Aktuální články z profilujících světových časopisů, např.: Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, Machine Learning, apod. |