Abstract
As the popularity of micro-blogging sites, followee recommendation plays an important role in information sharing over microblogging platforms. But as the popularity of microblogging sites increases, the difficulty of deciding who to follow also increases. The interests and emotions of users are often varied in their real lives. On the contrary, some other features of micro-blog are always unchangeable and they cannot describe the users characteristics very well. To solve this problem, we propose a personality-aware followee recommendation model (PSER) based on text semantics and sentiment analysis, a novel personality followee recommendation scheme over microblogging systems based on user attributes and the big-five personality model. It quantitatively analyses the effects of user personality in followee selection by combining personality traits with text semantics of micro-blogging and sentiment analysis of users. We conduct comprehensive experiments on a large-scale dataset collected from Sina Weibo, the most popular mircoblogging system in China. The results show that our scheme greatly outperforms existing schemes in terms of precision and an accurate appreciation of this model tied to a quantitative analysis of personality is crucial for potential followees selection, and thus, enhance recommendation.
Y. Fan—This work is supported by the National Nature Science Foundation (Grant No. 61472329 and 61532009), the Key Natural Science Foundation of Xihua University (Z1412620) and the Innovation Fund of Postgraduate, Xihua University.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tommasel, A., Corbellini, A., Godoy, D., et al.: Personality-aware followee recommendation algorithms: an empirical analysis. Eng. Appl. Artif. Intell. 51(C), 24–36 (2016)
Selfhout, M., Burk, W., Branje, S., et al.: Emerging late adolescent friendship networks and Big Five personality traits: a social network approach. J. Pers. 78(2), 509–538 (2010)
Tkalcic, M., Kunaver, M., Tasic, J., et al.: Personality based user similarity measure for a collaborative recommender system. In: Proceedings of the 5th Workshop on Emotion in Human-Computer Interaction-Real world challenges, pp. 30–37 (2009)
Gao, R., Hao, B., Bai, S., et al. Improving user profile with personality traits predicted from social media content. In: ACM Conference on Recommender Systems, pp. 355–358. ACM (2013)
Hamburger, Y.A., Benartzi, E.: The relationship between extraversion and neuroticism and the different uses of the Internet. Comput. Hum. Behav. 16(4), 441–449 (2000)
Quercia, D., Lambiotte, R., Stillwell, D., et al.: The personality of popular Facebook users. In: Acm Conference on Computer Supported Cooperative Work, pp. 955–964. DBLP (2012)
Mairesse, F., Walker, M.A., Mehl, M.R., et al.: Using linguistic cues for the automatic recognition of personality in conversation and text. J. Artif. Intell. Res. 30(1), 457–500 (2007)
Hu, R., Pu, P.: Enhancing collaborative filtering systems with personality information. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 197–204. ACM (2011)
Wu, W., Chen, L., He, L.: Using personality to adjust diversity in recommender systems. In: ACM Conference on Hypertext and Social Media, 225–229. ACM (2013)
Gou, L., You, F., Guo, J., et al.: SFViz: interest-based friends exploration and recommendation in social networks. In: Visual Information Communication - International Symposium. ACM (2011). Article no. 15
Yang, T., Cui, Y.D., Jin, Y.H.: BPR-UserRec: a personalized user recommendation method in social tagging systems. J. China Univ. Posts Telecommun. 20(1), 122–128 (2013)
Godin, F., Slavkovikj, V., Neve, W.D., et al. Using topic models for Twitter hashtag recommendation, pp. 593–596 (2013)
Xiong, X.B., Zhou, G., Huang, Y.Z., et al.: Dynamic evolution of collective emotions in social networks: a case study of Sina weibo. Sci. China Inf. Sci. 56(7), 1–18 (2013)
Sui, X., Lee, T.-H., Whang, J.J., Savas, B., Jain, S., Pingali, K., Dhillon, I.: Parallel clustered low-rank approximation of graphs and its application to link prediction. In: Kasahara, H., Kimura, K. (eds.) LCPC 2012. LNCS, vol. 7760, pp. 76–95. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37658-0_6
Bao, S., Xu, S., Zhang, L., et al. Joint emotion-topic modeling for social affective text mining. In: IEEE International Conference on Data Mining, pp. 699–704. IEEE (2009)
de Silva, N.F.F., Hruschka, E.R., Hruschka Jr., E.R.: Tweet sentiment analysis with classifier ensembles. Decis. Support Syst. 66, 170–179 (2014)
Hsu, W.H., King, A.L., Paradesi, M.S.R., et al.: Collaborative and structural recommendation of friends using weblog-based social network analysis. In: AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs, USA, pp. 55–60. AAAI (2006)
Chu, C.H., Wu, W.C., Wang, C.C., et al.: Friend Recommendation for location-based mobile social networks. In: Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing. IEEE Computer Society, pp. 365–370 (2013)
Feng, S., Zhang, L., Wang, D., et al.: A unified microblog user similarity model for online friend recommendation. Commu. Comput. Inf. Sci. 496, 286–298 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Xiao, P., Fan, Y., Du, Y. (2018). A Personality-Aware Followee Recommendation Model Based on Text Semantics and Sentiment Analysis. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-73618-1_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73617-4
Online ISBN: 978-3-319-73618-1
eBook Packages: Computer ScienceComputer Science (R0)