Skip to main content
SpringerLink
Log in

Exploiting user reviews for automatic movie tagging

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Auto-tagging movies with apt keywords/tags is essential and indispensable for online video providers. Tagged keywords are beneficial for movie promotion and recommendation. Currently, a popular approach is to propagate tags between similar videos identified by checking image similarity, which in principle takes each video as a sequence of frames. This approach is applicable for short video clips, however it is inefficient in processing long videos such as commercial movies, because it is very rare for two commercial movies to share a large portion of similar frames even if they belong to the same genre. In this work, we propose a novel scheme to auto-tag movies with two major steps. In the first step, we only consider popular movies with tremendous amount of attentions from online users, and we tag them by extracting keywords from user reviews. In the second step, unpopular movies are tagged by propagating the tags of similar popular movies to them. The similarity is evaluated based on multiple quantified attributes, including the movie summary, the title, the country, the genre and the tags, instead of frames to avoid expensive computation cost. To evaluate the performance of our scheme, we conduct experiments using data crawled from Douban, one of the largest movie rating websites in China. Experiment results demonstrate the superiority of our scheme by significantly improving tagging performance (in terms of Precision, Recall and F-Score) in comparison to baseline schemes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. We have removed redundant tags such as tags with the same meaning but in different languages, and meaningless tags such as actor names.

References

  1. Andre B, Vercauteren T, Buchner AM, Wallace MB, Ayache N (2012) Learning semantic and visual similarity for endomicroscopy video retrieval. IEEE Trans Med Imaging 31(6):1276–1288

    Article  Google Scholar 

  2. Ballan L, Bertini M, Uricchio T, Bimbo AD (2015) Data-driven approaches for social image and video tagging. Multimed Tools Appl 74(4):1443–1468

    Article  Google Scholar 

  3. Chen Z, Cao J, Song Y, Guo J, Zhang Y, Li J (2010) Context-oriented web video tag recommendation. Proc Www, pp 1079–1080

  4. Chen M, Zheng A, Weinberger KQ (2013) Fast image tagging. In: International conference on machine learning, pp III–1274

  5. El Aouad S, Dupuy C, Teixeira R, Bach F, Diot C (2016) Exploiting crowd sourced reviews to explain movie recommendation. In: Abdulla PA, Delporte-Gallet C (eds) Networked systems. Springer International Publishing, Cham, pp 193–201

  6. Jeon W, Cho S, Cha J, Byun H (2011) Tag suggestion using visual content and social tag. In: International conference on ubiquitous information management and communication, pp 1–5

  7. Jiang J, Zhang X, Loui AC (2011) A new video similarity measure model based on video time density function and dynamic programming. In: IEEE International conference on acoustics

  8. Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International conference on machine learning, pp II–1188

  9. Mihalcea R, Tarau P (2004) Textrank: bringing order into texts. Emnlp, 404–411

  10. Moxley E, Mei T, Hua XS, Ma WY, Manjunath B (2008) Automatic video annotation through search and mining. In: 2008 IEEE international conference on multimedia and expo. IEEE, pp 685–688

  11. Park J, Lee S, Lee SJ, Kim K, Chung BS, Yong KL (2011) Online video recommendation through tag-cloud aggregation. IEEE Multimed 18(1):78–87

    Article  Google Scholar 

  12. Pedro JS, Siersdorfer S, Sanderson M (2011) Content redundancy in youtube and its application to video tagging. Acm Trans Informa Syst 29(3):1–31

    Article  Google Scholar 

  13. Sevil SG, Kucuktunc O, Duygulu P, Can F (2010) Automatic tag expansion using visual similarity for photo sharing websites. Multimed Tools Appl 49(1):81–99

    Article  Google Scholar 

  14. Shan MK, Lee SY (1998) Content-based video retrieval based on similarity of frame sequence

  15. Shen J, Wang M, Chua TS (2016) Accurate online video tagging via probabilistic hybrid modeling. Multimed Syst 22(1):99–113

    Article  Google Scholar 

  16. Siersdorfer S, Pedro JS, Sanderson M (2009) Automatic video tagging using content redundancy. In: International ACM SIGIR conference on research and development in information retrieval, pp 395–402

  17. Tian F, Shen X, Liu X (2018) Multimedia automatic annotation by mining label set correlation. Multimed Tools Appl 77(3):3473–3491

    Article  Google Scholar 

  18. Tran HT, Fromont E, Jacquenet F, Jeudy B, Martins A (2013) Unsupervised video tag correction system. Extraction Et Gestion Des Connaissances Actes, 461–466

  19. Vijayanarasimhan S, Grauman K (2012) Active frame selection for label propagation in videos. In: European conference on computer vision, pp 496–509

  20. Wartena C, Brussee R, Wibbels M (2009) Using tag co-occurrence for recommendation. In: Ninth international conference on intelligent systems design & applications

  21. Wu L, Yang L, Yu N, Hua XS (2009) Learning to tag. In: Proceedings of the 18th international conference on world Wide Web, WWW ’09. ACM, New York, pp 361–370. https://doi.org/10.1145/1526709.1526758

  22. Wu B, Lyu S, Ghanem B (2015) Ml-mg: multi-label learning with missing labels using a mixed graph. In: The IEEE international conference on computer vision (ICCV)

  23. Yi K (2009) A study of evaluating the value of social tags as indexing terms. In: The international conference on knowledge management, pp 221–232

  24. Yu HF, Jain P, Kar P, Dhillon IS (2013) Large-scale multi-label learning with missing labels, 593–601

  25. Zhang ML, Zhou ZH (2007) Ml-knn: a lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048

    Article  Google Scholar 

  26. Zhang X, Huang Z, Shen HT, Yang Y, Li Z (2012) Automatic tagging by exploring tag information capability and correlation. World Wide Web-internet Web Inf Syst 15(3):233–256

    Article  Google Scholar 

  27. Zhao WL, Wu X, Ngo CW (2010) On the annotation of web videos by efficient near-duplicate search. IEEE Trans Multimed 12(5):448–461

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by ARC DE180100950, the National Natural Science Foundation of China under Grant U1911201, Guangdong Special Support Program under Grant 2017TX04X148, the Fundamental Research Funds for the Central Universities under Grant 19LGZD37, 19LGYJS57, 19LGYJS58.

Author information

Authors and Affiliations

  1. Department of Computer Science, Sun Yat-sen University, Guangzhou, China

    Canrui Wu, Chen Wang & Di Wu

  2. Key Laboratory of Machine Intelligence and Advanced Computing, (Sun Yat-sen University), Ministry of Education, Guangzhou, China

    Canrui Wu, Chen Wang & Di Wu

  3. Department of Computing, Macquarie University, Sydney, Australia

    Yipeng Zhou

  4. Computer Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Min Chen

  5. King Abdulaziz University, Jeddah, Saudi Arabia

    Min Chen

  6. Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing, China

    Jessie Hui Wang

  7. Beijing National Research Center for Information Science and Technology, Beijing, China

    Jessie Hui Wang

  8. School of Nursing, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Jing Qin

Authors
  1. Canrui Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yipeng Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Di Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Min Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jessie Hui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jing Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jessie Hui Wang.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, C., Wang, C., Zhou, Y. et al. Exploiting user reviews for automatic movie tagging. Multimed Tools Appl 79, 11399–11419 (2020). https://doi.org/10.1007/s11042-019-08513-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08513-0

Keywords

Search

Navigation