Advertisement

Searching Comprehensive Web Pages of Multiple Sentiments for a Topic

  • Shoko Wakamiya
  • Yukiko Kawai
  • Tadahiko Kumamoto
  • Jianwei Zhang
  • Yuhki Shiraishi
Chapter

Abstract

We have developed a novel system for searching comprehensive Web pages by focusing on multiplicity of sentiments of writers for a topic. Recently, lots of studies and services based on sentiment analysis have been conducted, since it is still difficult to search and summarize information satisfying users’ needs by text analysis only. In this paper, we propose a system for searching and visualizing comprehensive Web pages in terms of sentiments by extracting multiple sentiments of Web pages on a query topic and re-retrieving Web pages using sub-topic keywords. Specifically, this system extracts sentiment features of each Web page using a sentiment dictionary consisting of three sentiment dimensions; “Happy ⇔ Sad,” “Glad ⇔ Angry,” and “Peaceful ⇔ Strained.” Next, in order to conduct a re-retrieval, it extracts sub-topic keywords from Web pages of maximum (or minimum) sentiment features on three sentiment dimensions, respectively. Then, it re-retrieves Web pages using the query topic keyword and the extracted sub-topic keywords. Then, it plots them on sentiment graphs based on their sentiment features. By using the graphs, we can grasp not only sentiment tendency but also comprehensive sentiments for a query topic. In the experiment, we evaluate our proposed method using the developed system.

Keywords

Information retrieval Multiple sentiments Re-retrieval Sentiment analysis Sentiment dictionary Sentiment tendency 

Notes

Acknowledgments

This research was supported in part by Strategic Information and Communications R&D Promotion Programme (SCOPE), the Ministry of Internal Affairs and Communications of Japan, and JSPS KAKENHI Grant Numbers 24780248, 26280042, 26330347, 26330351, and 26870090.

References

  1. 1.
    Z. Zhuang, S. Cucerzan, Re-ranking search results using query logs, in Proceedings of the 15th ACM International Conference on Information and Knowledge Management, CIKM ’06, pp. 860–861 (2006)Google Scholar
  2. 2.
    T. Bogers, A. van den Bosch, in Authoritative Re-ranking of Search Results, ed. by M. Lalmas, A. MacFarlane, S. Rüger, A. Tombros, T. Tsikrika, A. Yavlinsky. Advances in Information Retrieval, Lecture Notes in Computer Science, vol. 3936, pp. 519–522 (2006)Google Scholar
  3. 3.
    J. Yan, N. Liu, E.Q. Chang, L. Ji, Z. Chen, Search result re-ranking based on gap between search queries and social tags, in Proceedings of the 18th International Conference on World Wide Web, WWW ’09, pp. 1197–1198 (2009)Google Scholar
  4. 4.
    S.K. Tyler, J. Wang, Y. Zhang, Utilizing re-finding for personalized information retrieval, in Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM ’10, pp. 1469–1472 (2010)Google Scholar
  5. 5.
    C. Kang, X. Wang, J. Chen, C. Liao, Y. Chang, B. Tseng, Z. Zheng, Learning to re-rank web search results with multiple pairwise features, in Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM ’11, pp. 735–744 (2011)Google Scholar
  6. 6.
    J. Xu, W.B. Croft, Query expansion using local and global document analysis, in Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’96, pp. 4–11 (1996)Google Scholar
  7. 7.
    Y. Lin, H. Lin, S. Jin, Z. Ye, Social annotation in query expansion: A machine learning approach, in Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’11, pp. 405–414 (2011)Google Scholar
  8. 8.
    B. Liu, Sentiment Analysis and Opinion Mining (Morgan & Claypool Publishers, Colorado, 2012)Google Scholar
  9. 9.
    B. Pang, L. Lee, Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  10. 10.
    I. Arapakis, J.M. Jose, P.D. Gray, Affective feedback: An investigation into the role of emotions in the information seeking process, in Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’08, pp. 395–402 (2008)Google Scholar
  11. 11.
    K. Eguchi, V. Lavrenko, Sentiment retrieval using generative models, in Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP ’06, pp. 345–354 (2006)Google Scholar
  12. 12.
    X. Huang, W.B. Croft, A unified relevance model for opinion retrieval, in Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM ’09, pp. 947–956 (2009)Google Scholar
  13. 13.
    M. Zhang, X. Ye, A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval, in Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’08, pp. 411–418 (2008)Google Scholar
  14. 14.
    K. Minami, S. Wakamiya, N. Hata, Y. Kawai, T. Kumamoto, J. Zhang, Y. Shiraishi, Comprehensive web search based on sentiment features, in Proceedings of the International MultiConference of Engineers and Computer Scientists 2014, IMECS 2014, 12–14 Mar 2014, Hong Kong. Lecture Notes in Engineering and Computer Science, pp. 483–488Google Scholar
  15. 15.
    J. Zhang, Y. Kawai, T. Kumamoto, S. Nakajima, Y. Shiraishi, Diverse sentiment comparison of news websites over time, in Proceedings of the 6th KES International Conference on Agent and Multi-Agent Systems: Technologies and Applications, KES-AMSTA’12, pp. 434–443 (2012)Google Scholar
  16. 16.
    W. Zhang, C. Yu, W. Meng, Opinion retrieval from blogs, in Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, CIKM ‘07, pp. 831–840 (2007)Google Scholar
  17. 17.
    Z. Luo, M. Osborne, T. Wang, Opinion retrieval in twitter, in Proceedings of the International AAAI Conference on Weblogs and Social Media (2012)Google Scholar
  18. 18.
    S. Chelaru, I.S. Altingovde, S. Siersdorfer, W. Nejdl, Analyzing, detecting, and exploiting sentiment in web queries. ACM Trans. Web 8(1), 6:1–6:28 (2013)Google Scholar
  19. 19.
    G. Demartini, S. Siersdorfer, Dear search engine: What’s your opinion about…?: Sentiment analysis for semantic enrichment of web search results, in Proceedings of the 3rd International Semantic Search Workshop, SEMSEARCH ’10, pp. 4:1–4:7 (2010)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Shoko Wakamiya
    • 1
  • Yukiko Kawai
    • 1
  • Tadahiko Kumamoto
    • 2
  • Jianwei Zhang
    • 3
  • Yuhki Shiraishi
    • 3
  1. 1.Kyoto Sangyo UniversityKyotoJapan
  2. 2.Chiba Institute of TechnologyNarashinoJapan
  3. 3.Tsukuba University of TechnologyTsukubaJapan

Personalised recommendations