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The latent learning model to derive semantic relations of words from unstructured text data in social media

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Abstract

Unstructured text data is very important in many applications because it reflects the thought of the people who create this data. However, it is difficult to realize the latent information as it was hidden on the unstructured text data. This paper proposes a latent learning method to construct the lexical structure to constitute the relations between the latent meaning and words. The established lexical structure derived the useful information from unstructured text data and this information and this information can be used for various application. This paper describes how to predict a rating from user-written reviews which is one of unstructured text data. And it also provides visualization information of the semantic lexical structures as the result of analysis. As a result, the proposed method easily quantifies the semantic relations of words and it shows good performance on prediction of ratings from unstructured text data. The proposed method can contribute to analyzing the unstructured text data in various perspectives on latent meaning of words.

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References

  1. Agarwal D, Chen B-C (2010) fLDA: matrix factorization through latent dirichlet allocation. In: Proceedings of the third ACM international conference on Web search and data mining, ACM, pp 91–100

  2. Alghunaim A (2015) A vector space approach for aspect-based sentiment analysis. Massachusetts Institute of Technology

  3. Baccianella S, Esuli A, Sebastiani F (2010) SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: LREC, pp 2200–2204

  4. Benton A, Paul MJ, Hancock B, Dredze M (2016) Collective supervision of topic models for predicting surveys with social media. In: AAAI, pp 2892–2898

  5. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022

    MATH  Google Scholar 

  6. He R, McAuley J (2016) Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, pp 507–517

  7. Kilgarriff A (2000) Wordnet: An electronic lexical database. JSTOR

  8. Koren Y, Bell R (2015) Advances in collaborative filtering. In: Recommender systems handbook. Springer, pp 77–118

  9. Li F, Liu NN, Jin H, Zhao K, Yang Q, Zhu X (2011) Incorporating reviewer and product information for review rating prediction. In: Twenty-Second International Joint Conference on Artificial Intelligence

  10. Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recommender system application developments: a survey. Decis Support Syst 74:12–32

    Article  Google Scholar 

  11. McAuley J, Leskovec J (2013) Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM conference on Recommender systems, ACM, pp 165–172

  12. McAuley J, Targett C, Shi Q, Van Den Hengel A (2015) Image-based recommendations on styles and substitutes. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp 43–52

  13. Meng L, Huang R, Gu J (2013) A review of semantic similarity measures in wordnet. IJHIT 6:1–12

    Google Scholar 

  14. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space arXiv preprint arXiv:13013781

  15. Moore TE (1982) Subliminal advertising: What you see is what you get. J Mark 46:38–47

  16. Pang B, Lee L (2008) Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval 2:1–135

    Article  Google Scholar 

  17. Qiu G, Liu B, Bu J, Chen C (2011) Opinion word expansion and target extraction through double propagation. Comput Linguist 37:9–27

    Article  Google Scholar 

  18. Ramage D, Hall D, Nallapati R, Manning CD (2009) Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1, Association for Computational Linguistics, pp 248–256

  19. Rosen-Zvi M, Griffiths T, Steyvers M, Smyth P (2004) The author-topic model for authors and documents. In: Proceedings of the 20th conference on Uncertainty in artificial intelligence, AUAI Press, pp 487–494

  20. Seo J, Choi S, Kim M, Han S (2013) The method of personalized recommendation with ensemble combination. JoWUA 4:108–121

    Google Scholar 

  21. Seo J, Choi S, Kim M, Han S (2016) A robust ensemble-based trust and reputation system against different types of intruder attacks. Int J Comput Math 93:308–324

    Article  MathSciNet  MATH  Google Scholar 

  22. Sivic J, Russell BC, Efros AA, Zisserman A, Freeman WT (2005) Discovering objects and their location in images. In: Computer Vision, ICCV 2005. Tenth IEEE International Conference on, 2005. IEEE, pp 370–377

  23. Tang D, Wei F, Qin B, Zhou M, Liu T (2014a) Building large-scale twitter-specific sentiment lexicon: A representation learning approach. In: COLING, pp 172–182

  24. Tang D, Wei F, Yang N, Zhou M, Liu T, Qin B (2014b) Learning sentiment-specific word embedding for twitter sentiment classification. In: ACL (1), pp 1555–1565

  25. Tang D, Qin B, Liu T, Yang Y (2015) User modeling with neural network for review rating prediction. In: IJCAI, pp 1340–1346

  26. Tsang AS, Prendergast G (2009) Is a “star” worth a thousand words? The interplay between product-review texts and rating valences. Eur J Mark 43:1269–1280

    Article  Google Scholar 

  27. Wang H, Lu Y, Zhai C (2010) Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 783–792

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Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT and future Planning (NRF - 2015R1A2 A2A01005304) and this research was supported by the Chung-Ang University Graduate Research Scholarship in 2015.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Chung-Ang University, 47, Heukeok-ro, Seoul, Republic of Korea

    Jiwan Seo, Karam Yoo, Seungjin Choi, Yura Alex Kim & Sangyong Han

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  1. Jiwan Seo
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  2. Karam Yoo
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  3. Seungjin Choi
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  4. Yura Alex Kim
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  5. Sangyong Han
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Correspondence to Sangyong Han.

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Seo, J., Yoo, K., Choi, S. et al. The latent learning model to derive semantic relations of words from unstructured text data in social media. Multimed Tools Appl 78, 28649–28663 (2019). https://doi.org/10.1007/s11042-018-6211-2

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  • DOI: https://doi.org/10.1007/s11042-018-6211-2

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