Abstract
In social network analysis, link prediction is an important area where the researchers can find the missing links and the future links possible among the users. Often, link prediction is made by analyzing the social linkage of the users in the given networks, i.e., the Topological structure of the networks. However, this approach leads to inconsistencies when researchers want to emphasize topics on which users have mainly engaged their selves in discussions. Mainly, this approach predicts future links based on available network structures without considering the topics on which the users are participating. This can be enhanced by incorporating the sentiment attributes and the community structure of the users in the network. In this paper, we propose an algorithm that incorporates the sentiment attribute of users and community structures along with the topological features. To evaluate the same, we have crawled the tweets of various countries concerning COVID-19 from Twitter. Experimental results show that users exhibiting the same emotion and belonging to the same community will influence other users to connect, thereby improving the performance of the link prediction.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.Data availability
The data sets analysed during the current study are available with the authors. The same will be shared once the manuscript is accepted/published.
References
Amancio DR, Oliveira ON Jr, Costa LdF (2012) Structure-semantics interplay in complex networks and its effects on the predictability of similarity in texts. Physica A: Stat Mech Appl 391(18):4406–4419
Amancio DR, da F Costa L et al (2015) Topological-collaborative approach for disambiguating authors’ names in collaborative networks. Scientometrics 102(1):465–485
Baccianella S, Esuli A, Sebastiani F (2010) Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Lrec, vol 10, pp 2200–2204
Bastami E, Mahabadi A, Taghizadeh E (2019) A gravitation-based link prediction approach in social networks. Swarm Evol Comput 44:176–186. https://doi.org/10.1016/j.swevo.2018.03.001
Bolla M (2011) Penalized versions of the newman-girvan modularity and their relation to normalized cuts and k-means clustering. Phys Rev E 84(1):016108. https://doi.org/10.1103/PhysRevE.84.016108
Coşkun M, Baggag A, Koyutürk M (2021) Fast computation of katz index for efficient processing of link prediction queries. Data Min Knowl Discov 35(4):1342–1368
De Meo P, Ferrara E, Fiumara G, Provetti A (2011) Generalized louvain method for community detection in large networks. In: 2011 11th international conference on intelligent systems design and applications. IEEE, pp 88–93. https://doi.org/10.1109/ISDA.2011.6121636
De Sá HR, Prudêncio RB (2011) Supervised link prediction in weighted networks. In: The 2011 international joint conference on neural networks. IEEE, pp 2281–2288. https://doi.org/10.1109/IJCNN.2011.6033513
Hu X, Tang L, Tang J, Liu H (2013) Exploiting social relations for sentiment analysis in microblogging. In: Proceedings of the sixth ACM international conference on Web search and data mining, pp 537–546. https://doi.org/10.1145/2433396.2433465
Jalili M, Orouskhani Y, Asgari M, Alipourfard N, Perc M (2017) Link prediction in multiplex online social networks. R Soc Open Sci 4(2):160863. https://doi.org/10.1098/rsos.160863
Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58(7):1019–1031. https://doi.org/10.1002/asi.20591
Liu B (2012) Sentiment analysis and opinion mining. Synth Lectures Hum Lang Technol 5(1):1–167. https://doi.org/10.2200/S00416ED1V01Y201204HLT016
Lü L, Zhou T (2011) Link prediction in complex networks: a survey. Physica A: Stat Mech Appl 390(6):1150–1170. https://doi.org/10.1016/j.physa.2010.11.027
Mallek S, Boukhris I, Elouedi Z, Lefèvre E (2019) Evidential link prediction in social networks based on structural and social information. J Comput Sci 30:98–107. https://doi.org/10.1016/j.jocs.2018.11.009
Marinho VQ, Hirst G, Amancio DR (2016) Authorship attribution via network motifs identification. In: 2016 5th Brazilian conference on intelligent systems (BRACIS). IEEE, pp 355–360
McCallum A, Corrada-Emmanuel A, Wang X (2005) Topic and role discovery in social networks. https://scholarworks.umass.edu/cs_faculty_pubs/3
Naik D, Gorojanam NB, Ramesh D (2020) Community based emotional behaviour using ekman’s emotional scale. In: International conference on innovations for community services. Springer, pp 63–82. https://doi.org/10.1007/978-3-030-37484-6_4
Samad A, Qadir M, Nawaz I (2019) Sam: a similarity measure for link prediction in social network. In: 2019 13th international conference on mathematics, actuarial science, computer science and statistics (MACS). IEEE, pp 1–9. https://doi.org/10.1109/MACS48846.2019.9024762
Santos DLB, Corrêa Jr EA, Oliveira Jr ON, Amancio DR, Mansur LL, Aluísio SM (2017) Enriching complex networks with word embeddings for detecting mild cognitive impairment from speech transcripts. Preprint at arXiv:1704.08088
Soundarajan S, Hopcroft J (2012) Using community information to improve the precision of link prediction methods. In: Proceedings of the 21st international conference on world wide web, pp 607–608. https://doi.org/10.1145/2187980.2188150
Tan C, Lee L, Tang J, Jiang L, Zhou M, Li P (2011) User-level sentiment analysis incorporating social networks. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 1397–1405. https://doi.org/10.1145/2020408.2020614
Tang E (1996) A cultural framework of “chinese learn english’’: a critical review of and reflections on. Compar Educ Rev 41(1):3–26
Thelwall M (2010) Emotion homophily in social network site messages. First Monday. https://doi.org/10.5210/fm.v15i4.2897
Tuan TM, Chuan PM, Ali M, Ngan TT, Mittal M et al (2019) Fuzzy and neutrosophic modeling for link prediction in social networks. Evol Syst 10(4):629–634. https://doi.org/10.1007/s12530-018-9251-y
Valverde-Rebaza J, de Andrade Lopes A (2012) Structural link prediction using community information on twitter. In: 2012 fourth international conference on computational aspects of social networks (CASoN). IEEE, pp 132–137. https://doi.org/10.1109/CASoN.2012.6412391
Wahid-Ul-Ashraf A, Budka M, Musial-Gabrys K (2017) Newton’s gravitational law for link prediction in social networks. In: International conference on complex networks and their applications. Springer, pp 93–104. https://doi.org/10.1007/978-3-319-72150-7_8
Wang H, Zhang F, Hou M, Xie X, Guo M, Liu Q (2018) Shine: signed heterogeneous information network embedding for sentiment link prediction. In: Proceedings of the eleventh ACM international conference on web search and data mining, pp 592–600. https://doi.org/10.1145/3159652.3159666
Wang Z, Wu Y, Li Q, Jin F, Xiong W (2016) Link prediction based on hyperbolic mapping with community structure for complex networks. Physica A: Stat Mech Appl 450:609–623. https://doi.org/10.1016/j.physa.2016.01.010
Wu H, Sorathia V, Prasanna VK(2012) Predict whom one will follow: followee recommendation in microblogs. In: 2012 international conference on social informatics. IEEE, pp 260–264. https://doi.org/10.1109/SocialInformatics.2012.74
Yang B, Cheung W, Liu J (2007) Community mining from signed social networks. IEEE Trans Knowl Data Eng 19(10):1333–1348. https://doi.org/10.1109/TKDE.2007.1061
Yu Y, Wang X (2014) Link prediction in directed network and its application in microblog. Math Probl Eng. https://doi.org/10.1155/2014/509282
Zeng J, Zhang S, Wu C (2008) A framework for www user activity analysis based on user interest. Knowl-Based Syst 21(8):905–910. https://doi.org/10.1016/j.knosys.2008.03.049
Zhang M, Chen Y (2017) Weisfeiler-lehman neural machine for link prediction. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 575–583
Zhang M, Chen Y (2018) Link prediction based on graph neural networks. In: Advances in neural information processing systems, pp 5165–5175
Zhao Z, Feng S, Wang Q, Huang JZ, Williams GJ, Fan J (2012) Topic oriented community detection through social objects and link analysis in social networks. Knowl-Based Syst 26:164–173. https://doi.org/10.1016/j.knosys.2011.07.017
Zhou J, Kwan C (2018) Missing link prediction in social networks. In: International symposium on neural networks. Springer, pp 346–354. https://doi.org/10.1007/978-3-319-92537-0_40
Zhou T, Lü L, Zhang Y-C (2009) Predicting missing links via local information. Eur Phys J B 71(4):623–630. https://doi.org/10.1140/epjb/e2009-00335-8
Funding
Not applicable
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflict of interest.
Ethical approval
This article does not contain any studies with human participants performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Naik, D., Ramesh, D. & Gorojanam, N.B. Enhanced link prediction using sentiment attribute and community detection. J Ambient Intell Human Comput 14, 4157–4174 (2023). https://doi.org/10.1007/s12652-022-04507-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-022-04507-3