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Distributed Multimodal Aspective on Topic Model Using Sentiment Analysis for Recognition of Public Health Surveillance

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Expert Clouds and Applications

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 209))

Abstract

Social media is the leading platform to accomplish large data in the field of health discipline tweets everywhere in the world empowerment currently. And it is a prominent data source for searching health terms (topics) and predicts solutions in the direction of health care. Health care has become one of the largest sectors in the world in terms of income and employment. Billions of customers use Twitter daily to enable people to share health-related topics of their views and opinions on various healthcare topics. Topic models commence from natural language processing (NLP) to acquiring immeasurable knowledge on healthcare areas which highly motivated to analyze the topic models (TM). Topic models are addressed intended for the squeezing of health topics for modeling the selective latent tweet documents in the healthcare system. Analyzing the topics in TM is an essential issue and facilitates an unreliable number of topics in TM that address the destitute results in health-related clustering (HRC) in various structured and unstructured data. In this regard, needful visualizations are imperative measurements to clip** the information for identifying cluster direction. So that, to believe and contribute proposed distributed multimodal active topic models such as Hadoop distributed non-negative matrix factorization (HdinNMF), Hadoop distributed latent Dirichlet allocation (HdiLDA), and Hadoop distributed probabilistic latent schematic indexing (HdiPLSI) are reasonable approaches for balancing and clip** to the direction of health topics from various perspective data sources in health statistics clustering. Hadoop DiNNMF distributed model is achieved and covered by cosine metrics when exposed to visual clusters and good performance measures compared to other methods in a series of health conditions. This assistance briefly describes the public health structure (hashtags) in good condition in the country and tracks the evolution of the main health-related tweets for preliminary advice to the public.

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References

  1. Rashid, J. et al.: Topic modeling technique for text mining over biomedical text corpora. https://doi.org/10.1109/ACCESS.2019.2944973

  2. Holzinger, A., Schantl, J., Schroettner, M., Seifert, C., Verspoor, K.: Biomedical text mining: state-of-the-art, open problems and future challenges. In: Interactive Knowledge Discovery and Data Mining in Biomedical Informatics, pp. 271–300. Springer, Berlin, Germany (2014)

    Book  Google Scholar 

  3. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  4. Landauer, T.K., Laham, D., Rehder, B., Schreiner, M.E.: A comparison of latent semantic analysis and humans. In: Proceedings of 19th Annual Meeting Cognition Science and Society, pp. 412–417 (1997)

    Google Scholar 

  5. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Amer. Soc. Inf. Sci. 41(6), 391407 (1990)

    Article  Google Scholar 

  6. Dredze, M.: How social media will change public health. IEEE Intell Syst 27(4), 81–84 (2012)

    Article  Google Scholar 

  7. Neuhauser, A.: Health Care Harnesses Social Media. U.S. News, (2014). https://www.usnews.com/news/articles/2014/06/05/health-care-harnessessocial-media

  8. Hawn, C.: Take two aspirin and tweet me in the morning: how Twitter, Facebook, and other social media are resha** health care. Health Aff. 28(2), 361–368 (2009)

    Article  Google Scholar 

  9. Subbarayudu, Y., Patil, S., Ramyasree, B., Praveen Kumar, C., Geetha, G.: Assort-EHR graph based semi-supervised classification algorithm for mining health records. J. Adv. Res. Dyn. Control Syst. EID: 2-s2.0–85058439255

    Google Scholar 

  10. Agrawal, A.: What is wrong with topic modeling? (And how to fix it using search-based SE). IEEE Trans. Softw. Eng. (2016). https://doi.org/10.1016/j.infsof.2018.02.005

  11. Blei, D.M.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)

    Article  Google Scholar 

  12. Grifths, T.L., Steyvers, M., Tenenbaum, J.B.: Topics in semantic representation. Psychol. Rev. 114(2), 211–244 (2007)

    Article  Google Scholar 

  13. Wallach, H.M.: Topic modeling: beyond bag-of-words. In: Proceedings of ICML, pp. 977–984 (2006)

    Google Scholar 

  14. Grifths, T.L., Steyvers, M.: Finding scientific topics. Proc. Nat. Acad. Sci. USA 101(1), 5228–5235 (2004)

    Article  Google Scholar 

  15. Gürcan, F.: Major research topics in big data: a literature analysis from 2013 to 2017 using probabilistic topic models. In: Proceedings of the International Conference on Artificial Intelligent Data Process (IDAP), pp. 1–4 (2018)

    Google Scholar 

  16. Gurcan, F., Kose, C.: Analysis of software engineering industry needs and trends: Implications for education. Int. J. Eng. Educ. 33(4), 1361–1368 (2017)

    Google Scholar 

  17. Tian, K., Revelle, M., Poshyvanyk, D.: Using latent Dirichlet allocation for automatic categorization of software. In: Proceedings of the 6th IEEE International Working Conference on Mining Software Repositories, pp. 163–166 (May 2009)

    Google Scholar 

  18. Zhai, Z., Liu, B., Xu, H., Jia, P.: Constrained LDA for grou** product features in opinion mining. In: Proceedings of Pacific Asia Conference on Knowledge Discovery Data Mining, pp. 448–459 (2011)

    Google Scholar 

  19. Wu, Q., Zhang, C., Hong, Q., Chen, L.: Topic evolution based on LDA and HMM and its application in stem cell research. J. Inf. Sci. 40(5), 611–620 (2014)

    Article  Google Scholar 

  20. Bagheri, A., Saraee, M., de Jong, F.: ADM-LDA: An aspect detection model based on topic modelling using the structure of review sentences. J. Inf. Sci. 40(5), 621–636 (2014)

    Article  Google Scholar 

  21. Hong, L., Davison, B.D.: Empirical study of topic modeling in Twitter. In: Proceedings of the 1st Workshop Social Media Analysis, pp. 80–88 (Jul 2010)

    Google Scholar 

  22. Chen, Z., Huang, Y., Tian, J., Liu, X., Fu, K., Huang, T.: Joint model for subsentence-level sentiment analysis with Markov logic. J. Assoc. Inf. Sci. Technol. 66(9), 19131922 (2015)

    Article  Google Scholar 

  23. Liu, L., Tang, L., Dong, W., Yao, S., Zhou, W.: An overview of topic modeling and its current applications in bioinformatics. SpringerPlus 5, 1608 (2016)

    Article  Google Scholar 

  24. Cohen, R., Aviram, I., Elhadad, M., Elhadad, N.: Redundancy-aware topic modeling for patient record notes. PloS ONE, 9, (2014). Article no. e87555

    Google Scholar 

  25. Kintsch, W.: The potential of latent semantic analysis for machine grading of clinical case summaries. J. Biomed. Inform. 35(1), 37 (2002)

    Article  Google Scholar 

  26. Cohen, T., Blatter, B., Patel, V.: Simulating expert clinical comprehension: Adapting latent semantic analysis to accurately extract clinical concepts from psychiatric narrative. J. Biomed. Inform. 41(6), 10701087 (2008)

    Google Scholar 

  27. Yeh, J.-F., Wu, C.-H., Chen, M.-J.: Ontology-based speech act identication in a bilingual dialog system using partial pattern trees. J. Amer. Soc. Inf. Sci. Technol. 59(5), 684694 (2008)

    Article  Google Scholar 

  28. Ginter, F., Suominen, H., Pyysalo, S., Salakoski, T.: Combining hidden Markov models and latent semantic analysis for topic segmentation and labeling: method and clinical

    Google Scholar 

  29. Haoxiang, W.: Emotional analysis of bogus statistics in social media. J. Ubiquitous Comput. Commun. Technol. (UCCT) 2(03), 178–186 (2020)

    Google Scholar 

  30. Yan, X., Guo, J.: Learning topics in short text using ncut-weighted non-negative matrix factorization on term correlation matrix

    Google Scholar 

  31. Yan, X., Guo, J.: Clustering short text using Ncut-weighted non-negative matrix factorization. In: Proceedings CIKM 2012, pp. 2259–2262. HI, USA, Miami (2012)

    Google Scholar 

  32. REC2015. https://trec.nist.gov/pubs/trec24/trec2015.html

  33. TREC2014. https://trec.nist.gov/pubs/trec23/trec2014.HTML

  34. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichletallocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  35. Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)

    Article  Google Scholar 

  36. Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Annual Conference on Neural Information Processing Systems, pp. 556–562 (2000)

    Google Scholar 

  37. Choo, J., Lee, C., Reddy, C.K., Park, H.: Utopian: user-driven topic modeling based on interactive nonnegative matrix factorization. IEEE Trans. Visual Comput. Graph. 19(12), 1992–2001 (2013)

    Article  Google Scholar 

  38. Lee, D., Seung, H.: Algorithms for non-negative matrix factorization. In: Advances in Neural İnformation Processing Systems 13, NIPS 2000, pp 556–562. Denver, CO, USA (2000)

    Google Scholar 

  39. Pattanodom, M., Iam-On N., Boongoen, T.: Clustering data with the presence of missing values by ensemble approach. In: 2016 Second Asian Conference on Defense Technology (ACDT) (2016). https://doi.org/10.1109/acdt.2016.7437660

  40. Amelio, A., Pizzuti, C.: Is normalized mutual information a fair measure for comparing community detection methods? In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2015)

    Google Scholar 

  41. Xu, G., Meng, Y., Chen, Z., Qiu, X., Wang, C., Yao, H.: Research on topic detection and tracking for online news texts. IEEE Access 7, 58407–58418 (2019). https://doi.org/10.1109/access.2019.2914097

    Article  Google Scholar 

  42. Huang, L., Ma, J., Chen, C.: Topic detection from microblogs using T-LDA and perplexity. In: 24th Asia-Pacific Software Engineering Conference Workshops (2017)

    Google Scholar 

  43. Li, Z., Shang, W., Yan, M.: News text classification model based on-topic model. In: 2016 IEEE/ACIS 15th International Conference on Computer And İnformation Science (ICIS) (2016)

    Google Scholar 

  44. Al Amin, H.M., Arefin, M.S., Dhar, P.K.: A method for video categorization by analyzing text, audio, and frames. Int. J. Inf. Tecnol. https://doi.org/10.1007/s41870-019-00338-2

  45. Bashar, A.: Intelligent development of big data analytics for manufacturing industry in cloud computing. J. Ubiquitous Comput. Commun. Technol. (UCCT) 1(01), 13–22 (2019)

    Google Scholar 

Download references

Acknowledgements

The research work supported the governmental and private health organizations, junior and senior researchers, and research and development of the health science institutions, for their excellent advice throughout the study.

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Authors and Affiliations

  1. Computer Science and Engineering, Jawaharlal Nehru Technological University, Anantapur, Andhra Pradesh, India

    Yerragudipadu Subbarayudu

  2. Computer Science and Engineering, JNTUA College of Engineering, Anantapur, Andhra Pradesh, India

    Alladi Sureshbabu

Authors
  1. Yerragudipadu Subbarayudu
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  2. Alladi Sureshbabu
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, GITAM School of Technology, GITAM University, Bengaluru, India

    I. Jeena Jacob

  2. Electrical Power Engineering, University of South-Eastern Norway, Notodden, Norway

    Francisco M. Gonzalez-Longatt

  3. Department of Mathematics and Computer Science, Concordia University Chicago, River Forest, IL, USA

    Selvanayaki Kolandapalayam Shanmugam

  4. Department of Publishing Information Technologies, Lviv Polytechnic National University, Lviv, Ukraine

    Ivan Izonin

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Subbarayudu, Y., Sureshbabu, A. (2022). Distributed Multimodal Aspective on Topic Model Using Sentiment Analysis for Recognition of Public Health Surveillance. In: Jeena Jacob, I., Gonzalez-Longatt, F.M., Kolandapalayam Shanmugam, S., Izonin, I. (eds) Expert Clouds and Applications. Lecture Notes in Networks and Systems, vol 209. Springer, Singapore. https://doi.org/10.1007/978-981-16-2126-0_38

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