skip to main content
research-article

Social Relationship Analysis Using State-of-the-art Embeddings

Published:08 May 2023Publication History
Skip Abstract Section

Abstract

Detection of human relationships from their interactions on social media is a challenging problem with a wide range of applications in different areas, like targeted marketing, cyber-crime, fraud, defense, planning, and human resource, to name a few. All previous work in this area has only dealt with the most basic types of relationships. The proposed approach goes beyond the previous work to efficiently handle the hierarchy of social relationships. This article introduces a novel technique named Quantifiable Social Relationship (QSR) analysis for quantifying social relationships to analyze relationships between agents from their textual conversations. QSR uses cross-disciplinary techniques from computational linguistics and cognitive psychology to identify relationships. QSR utilizes sentiment and behavioral styles displayed in the conversations for mapping them onto level II relationship categories. Then, for identifying the level III relationship categories, QSR uses level II relationships, sentiments, interactions, and word embeddings as key features. QSR employs natural language processing techniques for feature engineering and state-of-the-art embeddings generated by word2vec, global vectors (glove), and bidirectional encoder representations from transformers (bert). QSR combines the intrinsic conversational features with word embeddings for classifying relationships. QSR achieves an accuracy of up to 89% for classifying relationship subtypes. The evaluation shows that QSR can accurately identify the hierarchical relationships between agents by extracting intrinsic and extrinsic features from textual conversations between agents.

REFERENCES

  1. [1] Abbasi Ahmad, Javed Abdul Rehman, Chakraborty Chinmay, Nebhen Jamel, Zehra Wisha, and Jalil Zunera. 2021. ElStream: An ensemble learning approach for concept drift detection in dynamic social big data stream learning. IEEE Access 9 (2021), 6640866419.Google ScholarGoogle ScholarCross RefCross Ref
  2. [2] Akhtar Md Shad, Kumar Abhishek, Ghosal Deepanway, Ekbal Asif, and Bhattacharyya Pushpak. 2017. A multilayer perceptron based ensemble technique for fine-grained financial sentiment analysis. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 540546.Google ScholarGoogle ScholarCross RefCross Ref
  3. [3] Akhtar Shad, Ghosal Deepanway, Ekbal Asif, Bhattacharyya Pushpak, and Kurohashi Sadao. 2019. All-in-one: Emotion, sentiment and intensity prediction using a multi-task ensemble framework. IEEE Transactions on Affective Computing (2019).Google ScholarGoogle Scholar
  4. [4] Ashnai Bahar, Henneberg Stephan C., Naudé Peter, and Francescucci Anthony. 2016. Inter-personal and inter-organizational trust in business relationships: An attitude–behavior–outcome model. Industrial Marketing Management 52 (2016), 128139.Google ScholarGoogle ScholarCross RefCross Ref
  5. [5] Baron Robert A. and Byrne Donn. 2008. Social Psychology. Pearson; 12th edition, Bookvistas.Google ScholarGoogle Scholar
  6. [6] Bourgais Mathieu, Taillandier Patrick, and Vercouter Laurent. 2017. Enhancing the behavior of agents in social simulations with emotions and social relations. In International Workshop on Multi-agent Systems and Agent-Based Simulation. Springer, 89104.Google ScholarGoogle Scholar
  7. [7] Camacho David, Panizo-LLedot Angel, Bello-Orgaz Gema, Gonzalez-Pardo Antonio, and Cambria Erik. 2020. The four dimensions of social network analysis: An overview of research methods, applications, and software tools. Information Fusion (2020).Google ScholarGoogle ScholarCross RefCross Ref
  8. [8] Cambria Erik, Das Dipankar, Bandyopadhyay Sivaji, and Feraco Antonio. 2017. Affective computing and sentiment analysis. In A Practical Guide to Sentiment Analysis. Springer, 110.Google ScholarGoogle ScholarCross RefCross Ref
  9. [9] Cambria Erik, Howard Newton, Xia Yunqing, and Chua Tat-Seng. 2016. Computational intelligence for big social data analysis [guest editorial]. IEEE Computational Intelligence Magazine 11, 3 (2016), 89.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. [10] Cao Yang, Sharifi Golha, Upadrashta Yamini, and Vassileva Julita. 2003. Interpersonal relationships in group interaction in CSCW environments. In Proceedings of the User Modelling Workshop on Assessing and Adapting to User Attitudes and Affect (UM’03), Vol. 22.Google ScholarGoogle Scholar
  11. [11] Caragea Cornelia, Squicciarini Anna Cinzia, Stehle Sam, Neppalli Kishore, and Tapia Andrea H.. 2014. Mapping moods: Geo-mapped sentiment analysis during Hurricane Sandy. In ISCRAM.Google ScholarGoogle Scholar
  12. [12] Hunt Paul B. Horton Chester L.. 2004. Sociology. McGraw Hill Education, Bookvistas.Google ScholarGoogle Scholar
  13. [13] Cohen Sheldon, Mermelstein Robin, Kamarck Tom, and Hoberman Harry M.. 1985. Measuring the functional components of social support. In Social Support: Theory, Research and Applications. Springer, 7394.Google ScholarGoogle Scholar
  14. [14] Danescu-Niculescu-Mizil Cristian and Lee Lillian. 2011. Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs. In Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics. Association for Computational Linguistics, 7687.Google ScholarGoogle Scholar
  15. [15] Kimpe Lies De, Ponnet Koen, Walrave Michel, Snaphaan Thom, Pauwels Lieven, and Hardyns Wim. 2020. Help, I need somebody: Examining the antecedents of social support seeking among cybercrime victims. Computers in Human Behavior 108 (2020), 106310.Google ScholarGoogle ScholarCross RefCross Ref
  16. [16] Dobson David S. and Poels Karolien. 2020. Combined framing effects on attitudes and behavioral intentions toward mortgage advertisements. International Journal of Bank Marketing (2020).Google ScholarGoogle ScholarCross RefCross Ref
  17. [17] Gong Wenwen, Zhang Wei, Bilal Muhammad, Chen Yifei, Xu Xiaolong, and Wang Weizheng. 2021. Efficient web APIs recommendation with privacy-preservation for mobile app development in industry 4.0. IEEE Transactions on Industrial Informatics (2021), 11. Google ScholarGoogle ScholarCross RefCross Ref
  18. [18] Hulliyah Khodijah, Bakar Normi Sham Awang Abu, and Ismail Amelia Ritahani. 2017. Emotion recognition and brain mapping for sentiment analysis: A review. In 2017 2nd International Conference on Informatics and Computing (ICIC’17). IEEE, 15.Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Iqbal Farkhund, Batool Rabia, Fung Benjamin C. M., Aleem Saiqa, Abbasi Ahmed, and Javed Abdul Rehman. 2021. Toward tweet-mining framework for extracting terrorist attack-related information and reporting. IEEE Access 9 (2021), 115535115547.Google ScholarGoogle ScholarCross RefCross Ref
  20. [20] Kendall Philip C., Hollon Steven D., Beck Aaron T., Hammen Constance L., and Ingram Rick E.. 1987. Issues and recommendations regarding use of the beck depression inventory. Cognitive Therapy and Research 11, 3 (1987), 289299.Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Khan Mohib Ullah, Javed Abdul Rehman, Ihsan Mansoor, and Tariq Usman. 2020. A novel category detection of social media reviews in the restaurant industry. Multimedia Systems (2020), 114.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. [22] Khawaja Hussain S., Beg Mirza O., and Qamar Saira. 2018. Domain specific emotion lexicon expansion. In 2018 14th International Conference on Emerging Technologies (ICET’18). IEEE, 15.Google ScholarGoogle ScholarCross RefCross Ref
  23. [23] Kim Na-Young, Cha Yoonjung, and Kim Hea-Suk. 2019. Future English learning: Chatbots and artificial intelligence. Multimedia-assisted Language Learning 22, 3 (2019), 3253.Google ScholarGoogle Scholar
  24. [24] Kiritchenko Svetlana, Zhu Xiaodan, and Mohammad Saif M.. 2014. Sentiment analysis of short informal texts. Journal of Artificial Intelligence Research 50 (2014), 723762.Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] Kumar Prabhat, Gupta Govind P., and Tripathi Rakesh. 2021. An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks. Computer Communications 166 (2021), 110124. Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Kumar Prabhat, Gupta Govind P., and Tripathi Rakesh. 2021. TP2SF: A trustworthy privacy-preserving secured framework for sustainable smart cities by leveraging blockchain and machine learning. Journal of Systems Architecture 115 (2021), 101954. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. [27] Lai, Yi-Yu, Neville Jennifer, and Goldwasser Dan. 2019. Transconv: Relationship embedding in social networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 41304138.Google ScholarGoogle Scholar
  28. [28] Li Wei, Shao Wei, Ji Shaoxiong, and Cambria Erik. 2022. BiERU: Bidirectional emotional recurrent unit for conversational sentiment analysis. Neurocomputing 467 (2022), 7382.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. [29] Lin Po-Hung and Zhang Yu. 2017. Social network interaction quantification and relationship trend analysis with multi-agent systems. In Proceedings of the Agent-directed Simulation Symposium. Society for Computer Simulation International, 12.Google ScholarGoogle Scholar
  30. [30] Magee Joe C. and Galinsky Adam D.. 2008. 8 social hierarchy: The self-reinforcing nature of power and status. Academy of Management Annals 2, 1 (2008), 351398.Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] Mehl Matthias R. and Pennebaker James W.. 2003. The sounds of social life: A psychometric analysis of students’ daily social environments and natural conversations. Journal of Personality and Social Psychology 84, 4 (2003), 857.Google ScholarGoogle ScholarCross RefCross Ref
  32. [32] Mooney Raymond, Brew Chris, Chien Lee-Feng, and Kirchhoff Katrin. 2005. Proceedings of human language technology conference and conference on empirical methods in natural language processing. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing.Google ScholarGoogle Scholar
  33. [33] Nagao Katashi and Takeuchi Akikazu. 1994. Social interaction: Multimodal conversation with social agents. In AAAI, Vol. 94. 2228.Google ScholarGoogle Scholar
  34. [34] Qamar Saira, Mujtaba Hasan, Majeed Hammad, and Beg Mirza Omer. 2021. Relationship identification between conversational agents using emotion analysis. Cognitive Computation 13, 3 (2021), 673687.Google ScholarGoogle Scholar
  35. [35] Rudek Krzysztof and Kozlak Jaroslaw. 2018. Prediction of the persistence of relationships in social networks, considering previous reciprocity and duration. In 2018 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC’18). IEEE, 6366.Google ScholarGoogle ScholarCross RefCross Ref
  36. [36] Rus Vasile. 2018. Intelligent Chatbot Using Deep Learning. Ph.D. Dissertation. University of Memphis.Google ScholarGoogle Scholar
  37. [37] Saif Hassan, He Yulan, and Alani Harith. 2012. Semantic sentiment analysis of twitter. In International Semantic Web Conference. Springer, 508524.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. [38] Singh Dhirendra, Padgham Lin, and Logan Brian. 2017. Integrating BDI agents with agent-based simulation platforms: (JAAMAS extended abstract). In Proceedings of the 16th Conference on Autonomous Agents and Multiagent Systems. 249250.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. [39] Singh T. and Kumari M.. 2020. BbNCPD-Bayesian belief Network Based Contextual Polarity Disambiguation in Sentiment Analysis. Research Square. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  40. [40] Song Jingcheng, Han Zhaoyang, Wang Weizheng, Chen Jingxue, and Liu Yining. 2022. A new secure arrangement for privacy-preserving data collection. Computer Standards & Interfaces 80 (2022), 103582.Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. [41] Stadtfeld Christoph, Takács Károly, and Vörös András. 2020. The emergence and stability of groups in social networks. Social Networks 60 (2020), 129145.Google ScholarGoogle ScholarCross RefCross Ref
  42. [42] Tang Jih-Hsin, Chen Ming-Chun, Yang Cheng-Ying, Chung Tsai-Yuan, and Lee Yao-An. 2016. Personality traits, interpersonal relationships, online social support, and Facebook addiction. Telematics and Informatics 33, 1 (2016), 102108.Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. [43] Wang Ting, Li Yaoyong, Bontcheva Kalina, Cunningham Hamish, and Wang Ji. 2006. Automatic extraction of hierarchical relations from text. In European Semantic Web Conference. Springer, 215229.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. [44] Wang Zhaoxia, Chong Chee Seng, Lan Landy, Yang Yinping, Ho Seng Beng, and Tong Joo Chuan. 2016. Fine-grained sentiment analysis of social media with emotion sensing. In 2016 Future Technologies Conference (FTC’16). IEEE, 13611364.Google ScholarGoogle ScholarCross RefCross Ref
  45. [45] Weiss Nicole H., Kiefer Reina, Goncharenko Svetlana, Raudales Alexa M., Forkus Shannon R., Schick Melissa R., and Contractor Ateka A.. 2022. Emotion regulation and substance use: A meta-analysis. Drug and Alcohol Dependence 230 (2022), 109131.Google ScholarGoogle ScholarCross RefCross Ref
  46. [46] Whitehead Matthew and Yaeger Larry. 2010. Sentiment mining using ensemble classification models. In Innovations and Advances in Computer Sciences and Engineering. Springer, 509514.Google ScholarGoogle ScholarCross RefCross Ref
  47. [47] Yu Mo, Si Wenjun, Song Guojie, Li Zhenhui, and Yen John. 2014. Who were you talking to-mining interpersonal relationships from cellphone network data. In 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’14). IEEE, 485490.Google ScholarGoogle ScholarCross RefCross Ref
  48. [48] Zehra Wisha, Javed Abdul Rehman, Jalil Zunera, Khan Habib Ullah, and Gadekallu Thippa Reddy. 2021. Cross corpus multi-lingual speech emotion recognition using ensemble learning. Complex & Intelligent Systems 7, 4 (2021), 18451854.Google ScholarGoogle ScholarCross RefCross Ref
  49. [49] Zelenko Dmitry, Aone Chinatsu, and Richardella Anthony. 2003. Kernel methods for relation extraction. Journal of Machine Learning Research 3, (Feb. 2003), 10831106.Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. [50] Zhou GuoDong, Su Jian, Zhang Jie, and Zhang Min. 2005. Exploring various knowledge in relation extraction. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05). 427434.Google ScholarGoogle Scholar

Index Terms

  1. Social Relationship Analysis Using State-of-the-art Embeddings

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Asian and Low-Resource Language Information Processing
      ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 22, Issue 5
      May 2023
      653 pages
      ISSN:2375-4699
      EISSN:2375-4702
      DOI:10.1145/3596451
      Issue’s Table of Contents

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 8 May 2023
      • Online AM: 1 June 2022
      • Accepted: 25 May 2022
      • Revised: 9 May 2022
      • Received: 5 February 2022
      Published in tallip Volume 22, Issue 5

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Full Text

    View this article in Full Text.

    View Full Text
    About Cookies On This Site

    We use cookies to ensure that we give you the best experience on our website.

    Learn more

    Got it!