skip to main content
research-article

Fine-grained Emotion Role Detection Based on Retweet Information

Authors Info & Claims
Published:16 October 2018Publication History
Skip Abstract Section

Abstract

User behaviors in online social networks convey not only literal information but also one’s emotional attitudes towards the information. To compute this attitude, we define the concept of emotion role as the concentrated reflection of a user’s online emotional characteristics. Emotion role detection aims to better understand the structure and sentiments of online social networks and support further analysis, e.g., revealing public opinions, providing personalized recommendations, and detecting influential users. In this article, we first introduce the definition of a fine-grained emotion role, which consists of two dimensions: emotion orientation (i.e., positive, negative, and neutral) and emotion influence (i.e., leader and follower). We then propose a Multi-dimensional Emotion Role Mining model (MERM) to determine a user’s emotion role in online social networks. Specifically, we tend to identify emotion roles by combining a set of features that reflect a user’s online emotional status, including degree of emotional characteristics, accumulated emotion preference, structural factor, temporal factor, and emotion change factor. Experiment results on a real-life micro-blog reposting dataset show that the classification accuracy of the proposed model can achieve up to 90.1%.

References

  1. Afra Abnar, Mansoureh Takaffoli, Reihaneh Rabbany, and Osmar R. Zaïane. 2015. SSRM: Structural social role mining for dynamic social networks. Soc. Netw. Anal. Min. 5, 1 (2015), 1--18.Google ScholarGoogle ScholarCross RefCross Ref
  2. Haji Binali and Vidyasagar Potdar. 2012. Emotion detection state of the art. In Proceedings of the CUBE International Information Technology Conference. ACM, 501--507. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Freimut Bodendorf and Carolin Kaiser. 2009. Detecting opinion leaders and trends in online social networks. In Proceedings of the 2nd ACM Workshop on Social Web Search and Mining. ACM, 65--68. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Felipe Bravo-Marquez, Marcelo Mendoza, and Barbara Poblete. 2013. Combining strengths, emotions and polarities for boosting Twitter sentiment analysis. In Proceedings of the 2nd International Workshop on Issues of Sentiment Discovery and Opinion Mining. ACM, 2. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Huihui Chen, Bin Guo, Zhiwen Yu, and Liming Chen. 2015. CrowdPic: A multi-coverage picture collection framework for mobile crowd photographing. In Proceedings of the 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, the 2015 IEEE 12th International Conference on Autonomic and Trusted Computing, and the 2015 IEEE 15th International Conference on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom’15). IEEE, 68--76.Google ScholarGoogle Scholar
  6. Huihui Chen, Bin Guo, Zhiwen Yu, Liming Chen, and Xiaojuan Ma. 2017. A generic framework for constraint-driven data selection in mobile crowd photographing. IEEE IoT J. 4, 1 (2017), 284--296.Google ScholarGoogle Scholar
  7. Anqi Cui, Haochen Zhang, Yiqun Liu, Min Zhang, and Shaoping Ma. 2013. Lexicon-based sentiment analysis on topical chinese microblog messages. In Semantic Web and Web Science. Springer, 333--344.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Kurt Junshean P. Espinosa and Alma Mae J. Bernales. 2014. Characterizing influence factors affecting emotion diffusion in facebook. In Proceedings of the World Congress on Engineering and Computer Science, Vol. 2.Google ScholarGoogle Scholar
  9. Noah E. Friedkin and Eugene C. Johnsen. 1999. Social influence networks and opinion change. Adv. Group Process. 16, 1 (1999), 1--29.Google ScholarGoogle Scholar
  10. K. L. Gwet. 2012. Benchmarking inter-rater reliability coefficients. Handbook of Inter-Rater Reliability: The Definitive Guide to Measuring the Extent of Agreement among Raters (3rd. ed.). Advanced Analytics, Gaithersburg, MD, 164--180.Google ScholarGoogle Scholar
  11. Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. 2009. The WEKA data mining software: An update. ACM SIGKDD Explor. Newslett. 11, 1 (2009), 10--18. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Xia Hu, Jiliang Tang, Huiji Gao, and Huan Liu. 2013. Unsupervised sentiment analysis with emotional signals. In Proceedings of the 22nd International Conference on World Wide Web. ACM, 607--618. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Xia Hu, Lei Tang, Jiliang Tang, and Huan Liu. 2013. Exploiting social relations for sentiment analysis in microblogging. In Proceedings of the 6th ACM International Conference on Web Search and Data Mining. ACM, 537--546. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Anup K. Kalia, Norbou Buchler, Diane Ungvarsky, Ramesh Govindan, and Munindar P. Singh. 2014. Determining team hierarchy from broadcast communications. In Proceedings of the International Conference on Social Informatics. Springer, 493--507.Google ScholarGoogle Scholar
  15. Suin Kim, JinYeong Bak, and Alice Oh. 2012. Discovering emotion influence patterns in online social network conversations. SIGWEB Newslett. (Autumn) 3 (2012), 1--3. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Fang-Fei Kuo, Meng-Fen Chiang, Man-Kwan Shan, and Suh-Yin Lee. 2005. Emotion-based music recommendation by association discovery from film music. In Proceedings of the 13th Annual ACM International Conference on Multimedia. ACM, 507--510. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Lu Lin, Jianxin Li, Richong Zhang, Weiren Yu, and Chenggen Sun. 2014. Opinion mining and sentiment analysis in social networks: A retweeting structure-aware approach. In Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC’14). IEEE, 890--895. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Mary L. McHugh. 2012. Interrater reliability: The kappa statistic. Biochem. Med. 22, 3 (2012), 276--282.Google ScholarGoogle Scholar
  19. Qiaozhu Mei, Xu Ling, Matthew Wondra, Hang Su, and ChengXiang Zhai. 2007. Topic sentiment mixture: Modeling facets and opinions in weblogs. In Proceedings of the 16th International Conference on World Wide Web. ACM, 171--180. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Andrius Mudinas, Dell Zhang, and Mark Levene. 2012. Combining lexicon and learning based approaches for concept-level sentiment analysis. In Proceedings of the 1st International Workshop on Issues of Sentiment Discovery and Opinion Mining. ACM, 5. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Subhabrata Mukherjee, Gaurab Basu, and Sachindra Joshi. 2013. Incorporating author preference in sentiment rating prediction of reviews. In Proceedings of the 22nd International Conference on World Wide Web. ACM, 47--48. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Bo Pang and Lillian Lee. 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 271. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002. Thumbs up?: Sentiment classification using machine learning techniques. In Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, Volume 10. Association for Computational Linguistics, 79--86. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Darren Quinn, Liming Chen, and Maurice Mulvenna. 2012. Social network analysis-A survey. Int. J. Ambient Comput. Intell. 4, 3 (2012), 46--58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Farag Saad. 2014. Baseline evaluation: An empirical study of the performance of machine learning algorithms in short snippet sentiment analysis. In Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business. ACM, 6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. M. Zubair Shafiq, Muhammad U. Ilyas, Alex X. Liu, and Hayder Radha. 2013. Identifying leaders and followers in online social networks. IEEE J. Select. Areas Commun. 31, 9 (2013), 618--628.Google ScholarGoogle ScholarCross RefCross Ref
  27. Juan Shi, Kin Keung Lai, Ping Hu, and Gang Chen. 2017. Understanding and predicting individual retweeting behavior: Receiver perspectives. Appl. Soft Comput. 60 (2017), 844--857. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Yoshihiko Suhara, Yinzhan Xu, and Alex’Sandy’ Pentland. 2017. Deepmood: Forecasting depressed mood based on self-reported histories via recurrent neural networks. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 715--724. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Abhay Sukumaran, Stephanie Vezich, Melanie McHugh, and Clifford Nass. 2011. Normative influences on thoughtful online participation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 3401--3410. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Chenhao Tan, Lillian Lee, Jie Tang, Long Jiang, Ming Zhou, and Ping Li. 2011. User-level sentiment analysis incorporating social networks. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1397--1405. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Duyu Tang, Bing Qin, Furu Wei, Li Dong, Ting Liu, and Ming Zhou. 2015. A joint segmentation and classification framework for sentence level sentiment classification. IEEE/ACM Trans. Audio Speech Lang. Process. 23, 11 (2015), 1750--1761. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Jie Tang, Yuan Zhang, Jimeng Sun, Jinhai Rao, Wenjing Yu, Yiran Chen, and A. C. M. Fong. 2012. Quantitative study of individual emotional states in social networks. IEEE Trans. Affect. Comput. 3, 2 (2012), 132--144. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Simo Editha Tchokni, Diarmuid O. Séaghdha, and Daniele Quercia. 2014. Emoticons and phrases: Status symbols in social media. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM’14).Google ScholarGoogle Scholar
  34. Di Wang, Alistair Sutcliffe, and Xiao-Jun Zeng. 2011. A trust-based multi-ego social network model to investigate emotion diffusion. Soc. Netw. Anal. Min. 1, 4 (2011), 287--299.Google ScholarGoogle ScholarCross RefCross Ref
  35. Jingang Wang, Dandan Song, Lejian Liao, Wei Zou, Xiaoqing Yan, and Yi Su. 2013. The chinese bag-of-opinions method for hot-topic-oriented sentiment analysis on weibo. In Semantic Web and Web Science. Springer, 357--367.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Zhitao Wang, Zhiwen Yu, Liming Chen, and Bin Guo. 2014. Sentiment detection and visualization of chinese micro-blog. In Proceedings of the 2014 International Conference on Data Science and Advanced Analytics (DSAA’14). IEEE, 251--257.Google ScholarGoogle ScholarCross RefCross Ref
  37. Dingqi Yang, Daqing Zhang, Zhiyong Yu, Zhiwen Yu, and Djamal Zeghlache. 2014. SESAME: Mining user digital footprints for fine-grained preference-aware social media search. ACM Trans. Internet Technol. 14, 4 (2014), 28. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Yang Yang, Jia Jia, Shumei Zhang, Boya Wu, Qicong Chen, Juanzi Li, Chunxiao Xing, and Jie Tang. 2014. How do your friends on social media disclose your emotions? In Association for the Advancement of Artificial Intelligence (AAAI), Vol. 14. 1--7. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Show-Jane Yen and Yue-Shi Lee. 2006. Under-sampling approaches for improving prediction of the minority class in an imbalanced dataset. Intelligent Control and Automation. Springer, Berlin, Heidelberg, 731--740.Google ScholarGoogle Scholar
  40. Fei Yi, Zhiwen Yu, Huihui Chen, He Du, and Bin Guo. 2018. Cyber-physical-social collaborative sensing: From single space to cross-space. Front. Comput. Sci. 12, 4 (2018), 609--622. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Zhiwen Yu, Zhitao Wang, Liming Chen, Bin Guo, and Wenjie Li. 2016. Featuring, detecting, and visualizing human sentiment in chinese micro-blog. ACM Trans. Knowl. Discov. Data 10, 4 (2016), 48. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Zhiwen Yu, Zhu Wang, Huilei He, Jilei Tian, Xinjiang Lu, and Bin Guo. 2015. Discovering information propagation patterns in microblogging services. ACM Trans. Knowl. Discov. Data 10, 1 (2015), 7. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Zhiwen Yu, Fei Yi, Qin Lv, and Bin Guo. 2018. Identifying on-site users for social events: Mobility, content, and social relationship. IEEE Trans. Mobile Comput., Issue No. 01, 1--14.Google ScholarGoogle Scholar
  44. Guangchao Yuan, Pradeep K. Murukannaiah, Zhe Zhang, and Munindar P. Singh. 2014. Exploiting sentiment homophily for link prediction. In Proceedings of the 8th ACM Conference on Recommender systems. ACM, 17--24. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Zhongwu Zhai, Hua Xu, and Peifa Jia. 2008. Identifying opinion leaders in BBS. In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008 (WI-IAT’08). Vol. 3. IEEE, 398--401. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Ying Zhang, Ning Zhang, Luo Si, Yanshan Lu, Qifan Wang, and Xiaojie Yuan. 2014. Cross-domain and cross-category emotion tagging for comments of online news. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 627--636. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Yuchen Zhao, Guan Wang, Philip S. Yu, Shaobo Liu, and Simon Zhang. 2013. Inferring social roles and statuses in social networks. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 695--703. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Fine-grained Emotion Role Detection Based on Retweet Information

        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 Internet Technology
          ACM Transactions on Internet Technology  Volume 19, Issue 1
          Regular Papers, Special Issue on Service Management for IOT and Special Issue on Knowledge-Driven BPM
          February 2019
          321 pages
          ISSN:1533-5399
          EISSN:1557-6051
          DOI:10.1145/3283809
          • Editor:
          • Ling Liu
          Issue’s Table of Contents

          Copyright © 2018 ACM

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 16 October 2018
          • Accepted: 1 February 2018
          • Revised: 1 January 2018
          • Received: 1 February 2017
          Published in toit Volume 19, Issue 1

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Research
          • Refereed

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader
        About Cookies On This Site

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

        Learn more

        Got it!