Context Aware Sentiment Link Prediction in Heterogeneous Social Network


People often express opinions towards others in a social network, causing sentiment links to form among users. To develop effective methods for discovering implicit sentiment links among users, the extraction and exploitation of structural semantic information from heterogeneous social networks are of great importance. We propose a novel heterogeneous social network embedding-based approach for sentiment link prediction that takes both global structural information with multi-dimensional relations and heterogeneous context information into consideration to leverage rich and intrinsic association information. Specifically, the attributed heterogeneous social network and Sentic LSTM-based sentiment link network are employed to incorporate various explicit context knowledge and implicit multi-dimensional user interaction association knowledge into representation learning and sentiment link prediction. The experimental results on a real-world dataset show that the proposed approach has advantages over the state-of-the-art baselines. The results show the effectiveness of incorporating social relations and profile context information into sentiment link prediction, especially in cold-start scenarios. The learned embedding representation that incorporates both structural information with multi-dimensional relations and context information from heterogeneous social networks can improve sentiment link prediction performance. The proposed approach is effective and feasible for detecting unobserved sentiment links from online social networks and outperforms the state-of-the-art baselines in sentiment link prediction tasks.

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  1. 1.

    Camacho D, Panizo-LLedot Á, Bello-Orgaz G, Gonzalez-Pardo A, Cambria E. The four dimensions of social network analysis: an overview of research methods, applications, and software tools. Information Fusion. 2020;6:88–120.

    Google Scholar 

  2. 2.

    Martínez V, Berzal F, Cubero JC. A survey of link prediction in complex networks. ACM Computing Surveys (CSUR). 2017;49(4):1–33.

    Article  Google Scholar 

  3. 3.

    Shi C, Li Y, Zhang J, et al. A survey of heterogeneous information network analysis. IEEE Trans on Knowl Data Eng. 2017;29(1):17–37.

    Article  Google Scholar 

  4. 4.

    Shi C, Syu  P. Heterogeneous information network analysis and applications anonymous translator 2017 1st edn Springer CH.

  5. 5.

    Grover A, Leskovec J. node2vec: scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016;13(17):855–864.

  6. 6.

    Ma Y, Peng H, Khan T, Cambria E, Hussain A. Sentic LSTM: a hybrid network for targeted aspect-based sentiment analysis. Cogn Comput. 2018;10(4):639–50.

    Article  Google Scholar 

  7. 7.

    Cambria E, Hussain A, Havasi C, Eckl C. Sentic computing: exploitation of common sense for the development of emotion-sensitive systems. In: Esposito A, Campbell N, Vogel C, Hussain A, Nijholt A. (eds) Development of Multimodal Interfaces: Active Listening and Synchrony. Lect Notes Comput Sci. 2010;5967. Springer, Berlin, Heidelberg.

  8. 8.

    Cambria E. Affective computing and sentiment analysis. IEEE Intell Syst. 2016;31(2):102–7.

    Article  Google Scholar 

  9. 9.

    Cambria E, Poria S, Hussain A, Liu B. Computational intelligence for affective computing and sentiment analysis [Guest Editorial]. IEEE Comput Intell Mag. 2019;14(2):16–7.

    Article  Google Scholar 

  10. 10.

    Cambria E, Li Y, Xing FZ, Poria S, Kwok K. SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM '20). Association for Computing Machinery. New York, NY, USA. 2020;105–114.

  11. 11.

    Al-Ghadir AI, Azmi AM, Hussain A. A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments. Information Fusion. 2021;67:29–40.

    Article  Google Scholar 

  12. 12.

    Akhtar MS, Ekbal A, Cambria E. How intense are you? Predicting intensities of emotions and sentiments using stacked ensemble [Application Notes]. IEEE Comput Intell Mag. 2020;15(1):64–75.

    Article  Google Scholar 

  13. 13.

    Wang P, Xu B, Wu Y, Zhou X. Link prediction in social networks: the state-of-the-art. Scientia Sinica Informationis. 2015;58(1):1–38.

    Article  Google Scholar 

  14. 14.

    Wang H, Zhang F, Hou M, Xie X, Guo M, Liu Q. Shine: Signed heterogeneous information network embedding for sentiment link prediction. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. 2018;592–600.

  15. 15.

    Yuan G, Murukannaiah PK, Zhang Z, Singh MP. Exploiting sentiment homophily for link prediction. Proceedings of the 8th ACM Conference on Recommender Systems. 2014;17–24.

  16. 16.

    Sharma P, Singh UK, Sharma TV, Das D. Algorithm for prediction of links using sentiment analysis in social networks. Proceedings of the 7th International Conference on Computing Communication and Networking Technologies. 2016;6(08):1–6.

  17. 17.

    Yoo S, Song J, Jeong O. Social media contents based sentiment analysis and prediction system. Expert Systems with Applications. 2018;105:102–11.

    Article  Google Scholar 

  18. 18.

    Shakibian H, Charkari NM. Mutual information model for link prediction in heterogeneous complex networks. Sci Report. 2017;7(1):44981.

    Article  Google Scholar 

  19. 19.

    Shakibian H, Charkari NM. Statistical similarity measures for link prediction in heterogeneous complex networks. Physica A: Statistical Mechanics and its Applications. 2018;501:248–63.

    Article  Google Scholar 

  20. 20.

    Li JC, Zhao DL, Ge BF, Yang KW, Chen YW. A link prediction method for heterogeneous networks based on bp neural network. Physica A: Statistical Mechanics and its Applications. 2018;495:1–17.

    Article  Google Scholar 

  21. 21.

    Shi C, Hu B, Zhao WX, Yu PS. Heterogeneous information network embedding for recommendation. IEEE Trans Knowl Data Eng. 2019;31(2):357–70.

    Article  Google Scholar 

  22. 22.

    Chen H, Yin H, Wang W, Wang H, Nguyen QV, Li X. PME: projected metric embedding on heterogeneous networks for link prediction. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018;1177–1186.

  23. 23.

    Zhuo W, Zhan Q, Liu Y, Xie Z, Lu J. Context attention heterogeneous network embedding. Comput Intell Neurosci. 2019;1–15.

  24. 24.

    Wang Y, Feng C, Chen L, Yin H, Guo C, Chu Y. User identity linkage across social networks via linked heterogeneous network embedding. World Wide Web. 2019;22(6):2611–32.

    Article  Google Scholar 

  25. 25.

    Fu TY, Lee WC, Lei Z. HIN2Vec: explore meta-paths in heterogeneous information networks for representation learning. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 2017;131841:1797–1806.

  26. 26.

    Wu B, Wen W, Hao Z, Cai R. Multi-context aware user–item embedding for recommendation. Neural Netw. 2020;124:86–94.

    Article  Google Scholar 

  27. 27.

    Cavallari S, Cambria E, Cai H, Chang KC, Zheng VW. Embedding both finite and infinite communities on graphs [Application Notes]. IEEE Comput Intell Mag. 2019;14(3):39–50.

    Article  Google Scholar 

  28. 28.

    Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed representations of words and phrases and their compositionality. Adv Neural Inf Proces Syst. 2013.

  29. 29.

    Zhang F, Yuan NJ, Lian D, Xie X, Ma WY. Collaborative knowledge base embedding for recommender systems. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016;13–17:353–362.

  30. 30.

    Wang P, Guo J, Lan Y, Xu J, Wan S, Cheng X. Learning hierarchical representation model for nextbasket recommendation. Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2015;403–412.

  31. 31.

    Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q. LINE: large-scale information network embedding. Proceedings of the 24th International Conference on World Wide Web. 2015;1067–1077.

  32. 32.

    Dong Y, Chawla NW, Swami A. metapath2vec: scalable representation learning for heterogeneous networks. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.  2017;129685:135–144.

  33. 33.

    Liao L, He X, Zhang H, Chua TS. Attributed social network embedding. IEEE Trans Knowl Data Eng. 2018;30(12):2257–70.

    Article  Google Scholar 

  34. 34.

    Yang S, Yang B. Enhanced network embedding with text information. Proceedings - International Conference on Pattern Recognition. 2018;326–331.

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This study was funded by the Chongqing Research Program of Basic Research and Frontier Technology (grant no. Cstc2018jcyjAX0708) and the Basic Scientific Research Projects of Wenzhou (grant no. G2020024).

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Correspondence to Anping Zhao.

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Zhao, A., Yu, Y. Context Aware Sentiment Link Prediction in Heterogeneous Social Network. Cogn Comput (2021).

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  • Sentiment link
  • Context
  • Network embedding
  • Heterogeneous social network