Skip to main content

Advertisement

SpringerLink
Log in

Domain-specific text dictionaries for text analytics

  • Regular Paper
  • Published:
International Journal of Data Science and Analytics Aims and scope Submit manuscript

Abstract

We investigate the use of sentiment dictionaries to estimate sentiment for large document collections. Our goal in this paper is a semiautomatic method for extending a general sentiment dictionary for a specific target domain in a way that minimizes manual effort. General sentiment dictionaries may not contain terms important to the target domain or may score terms in ways that are inappropriate for the target domain. We combine statistical term identification and term evaluation using Amazon Mechanical Turk to extend the EmoLex sentiment dictionary to a domain-specific study of dengue fever. The same approach can be applied to any term-based sentiment dictionary or target domain. We explain how terms are identified for inclusion or re-evaluation and how Mechanical Turk generates scores for the identified terms. Examples are provided that compare EmoLex sentiment estimates before and after it is extended. We conclude by describing how our sentiment estimates can be integrated into an epidemiology surveillance system that includes sentiment visualization and discussing the strengths and limitations of our work.

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

Similar content being viewed by others

Notes

  1. https://www.brandwatch.com, formerly Crimson Hexagon, a subscription service that provides “insights from 100 million sources and 1.4 trillion posts”

  2. The odds a particular outcome occurs given a particular exposure, versus the odds of the outcome absent the exposure.

  3. https://www.brandwatch.com

References

  1. Alharbi, M., Laramee, R.S.: SoS TextViz: an extend survey of surveys on text visualization. Computers 8(1), 143–152 (2019)

    Article  Google Scholar 

  2. Dou, W., Liu, S.: Topic- and time-oriented visual text analysis. IEEE Comput. Gr. Vis. 36(4), 8–13 (2016)

    Article  Google Scholar 

  3. Kucher, K., Paradis, C., Kerren, A.: State of the art in sentiment visualization. Comput. Gr. Forum 37(1), 71–96 (2017)

    Article  Google Scholar 

  4. Shepard, D.S., Halasa, Y.A., Tyagi, B.K., Adhish, S.V., Nandan, D., Karthiga, K.S., Chellaswamy, V., Gaba, M., Arora, N.K.: Economic and disease burden of dengue illness in India. Am. J. Trop. Med. Hyg. 91(6), 1235–1242 (2014)

    Article  Google Scholar 

  5. Plutchik, R.: A general psychoevolutionary theory of emotion. In: Plutchik, R., Kellerman, H. (eds.) Theories of Emotion : Emotion, Theory, Research, and Experience, pp. 3–31. Academic Press, New York (1980)

    Chapter  Google Scholar 

  6. Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)

    Article  Google Scholar 

  7. Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. In: Aggarwal, C.C., Zhai, C.X. (eds.) Mining Text Data, pp. 415–463. Springer, New York (2012)

    Chapter  Google Scholar 

  8. Mohammad, S.M.: Sentiment analysis: detecting valence, emotions, and other affectual states from text. In: Meiselman, H. (ed.) Emotional Measurement, pp. 201–237. Elsevier, Atlanta (2015)

    Google Scholar 

  9. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)

    Article  Google Scholar 

  10. Zhang, L., Wang, S., Liu, B.: Deep learning for sentiment analysis: a survey. WIREs Data Min. Knowl. Discov. 8(4), 1–25 (2018)

    Google Scholar 

  11. Russell, J.A.: A circumplex model of affect. J. Personal. Soc. Psychol. 39(6), 1161–1178 (1980)

    Article  Google Scholar 

  12. Russell, J.A., Feldman Barrett, L.: The structure of current affect: controversies and emerging consensus. Curr. Dir. Psychol. Sci. 8(1), 10–14 (1999)

    Article  Google Scholar 

  13. Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting of the association for computational linguistics (ACL ’04), Barcelona, Spain, pp. 271–278 (2004)

  14. Pang, B., Lee, L., Vithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the 2002 conference on empirical methods in natural language processing (EMNLP 2002), Philadelphia, PA, pp. 79–86 (2002)

  15. Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting of the association for computational linguistics (ACL ’02), Philadelphia, PA, pp. 417–424 (2002)

  16. Bonata, V., Janardhan, N.: A comprehensive study on lxicon based approaches for sentiment analysis. Asian J. Comput. Sci. Technol. 8(S2), 1–6 (2019)

    Article  Google Scholar 

  17. DiBattista, J.: The best python sentiment analysis package (\(+1\) Huge Mistake). https://towardsdatascience.com/the-best-python-sentiment-analysis-package-1-huge-common-mistake-d6da9ad6cdeb. Online; accessed 02 Mar 2021 (2021)

  18. Podiotis, P.: Sentiment analysis of the CIA world Factbook). Social science research network (SSRN), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3721400. Online; accessed 02 Mar 2021 (2020)

  19. Li, Z., Wei, Y., Zhang, Y., Yang, Q.: Hierarchical attention transfer network for cross-domain sentiment classification. In: Proceedings of the thirty-second AAAI conference on artifical intelligence (AAAI-18), New Orleans, LA, pp. 5852–5859 (2018)

  20. Zhang, K., Zhang, K., Zhang, M., Zhao, H., Liu, W., Wei, W.: Incorporating dynamic semantics into pre-trained language model for aspect-based sentiment analysis. In: Cohn, T., He, Y., Liu, Y. (eds.) Findings of the Association for Computational Linguistics (ACL 2022), pp. 3599–3610. Ireland, Dublin (2022)

    Chapter  Google Scholar 

  21. Kenton, J.D., Chang, M.-W., Toutanova, L.K.: BERT: Pre-training of deep bidirectional transforms for language understanding. In: Proceedings of the 2019 annual conference of the North American chapter of the association for computational linguistics-human language technologies (NAACL-HLT 2019), virtual, pp. 4171–4189 (2019)

  22. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems 33 (NeurlPS 2020), pp. 1877–1901. virtual, (2020)

  23. Lewis, M., Liu, Y., Goya, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., Zettlemoyer, L.: BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th annual meeting of the association for computational linguistics (ACL 2020), Seattle, Washington, pp. 7871–7880 (2020)

  24. Song, K., Tan, X., Qin, T., Lu, U., Y., L.T.: MASS: Masked sequence to sequence pre-training for language generation. In: Proceedings of the 36th international conference on machine learning (ICML 2019), Long Beach, California, pp. 5926–5936 (2019)

  25. Pepe, A., Bollen, J.: Between conjecture and memento: shaping a collective emotional perception of the future. In: AAAI spring symposium on emotion, personality, and social behavior, Stanford, CA, pp. 111–116 (2008)

  26. Mislove, A., Lehmann, S., Ahn, Y.-Y., Onnela, J.-P., Rosenquist, J.N.: Pulse of the Nation: U.S. Mood Throughout the Day Inferred from Twitter. http://www.ccs.neu.edu/home/amislove/twittermood (2010)

  27. Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. J. Am. Soc. Inf. Sci. Technol. 61(12), 2544–2558 (2010)

    Article  Google Scholar 

  28. Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 25–54 (2010)

    Article  Google Scholar 

  29. Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the 7th international conference on language resources and evaluation (LREC ’10), Valletta, Malta, pp. 2200–2204 (2010)

  30. Warriner, A.B., Kuperman, V., Brysbaert, M.: Norms of valence, arousal, and dominance for 13,915 English lemmas. Behav. Res. Methods 45(4), 1191–1207 (2013)

    Article  Google Scholar 

  31. Cao, N., Lin, Y.-R., Sun, X., Lazer, D., Liu, S., Huamin, Q.: Whisper: Tracing the spatiotemporal process of information diffusion in real time. IEEE Trans Vis. Comput. Gr. 18(12), 2649–2658 (2012)

    Article  Google Scholar 

  32. Cao, N., Lu, L., Lin, Y.-R., Wang, F.: SocialHelix: Visual analysis of sentiment divergence in social media. J. Vis. 18(2), 221–235 (2014)

    Article  Google Scholar 

  33. Wu, Y., Liu, S., Yan, K., Liu, M., Wu, F.: OpinionFlow: visual analysis of opinion diffusion on social media. IEEE Trans. Vis. Comput. Gr. 20(12), 1763–1772 (2014)

    Article  Google Scholar 

  34. Liu, Y., Wang, H., Landis, S., Macjejewski, R.: A visual analytics framework for identifying topic drivers in media events. IEEE Trans. Vis. Comput. Gr. 24(9), 2501–2515 (2017)

    Article  Google Scholar 

  35. El-Assady, M., Gold, V., Acevedo, C., Collins, C., Keim, D.: ConToVi: multi-party conversation exploration using topic-space views. Comput. Gr. Forum 35(3), 431–440 (2016)

    Article  Google Scholar 

  36. El-Assady, M., Sevastjanova, R., Keim, D., Collins, C.: ThreadReconstructor: modeling reply-chains to untangle conversational text through visual analytics. Comput. Gr. Forum 37(3), 351–365 (2018)

    Article  Google Scholar 

  37. Hoque, E., Carenini, G.: ConVis: a visual text analytic system for exploring blog conversations. Comput. Gr. Forum 33(3), 221–230 (2014)

    Article  Google Scholar 

  38. Hoque, E., Carenini, G.: MultiConVis: A visual text analysis system for exploring a collection of online conversations. In: Proceedings of the 21st international conference on intelligent user interfaces (IUI ’16), Sonoma, CA, pp. 96–107 (2016)

  39. Mohammad, S.M., Sobhani, P., Kiritchenko, S.: Stance and sentiment in tweets. ACM Trans. Int. Technol. 17(3), 26 (2017)

    Article  Google Scholar 

  40. Kucher, K., Martins, R.M., Paradis, C., Kerren, A.: StanceVis Prime: visual analysis of sentiment and stance in social media texts. J. Vis. 23(6), 1015–1034 (2020)

    Article  Google Scholar 

  41. Wei, F., Shixia, L., Yangqiu, S., Shimei, P., Zhou, M.X., Qian, W., Lei, S., Li, T., Qiang, Z.: TIARA: interactive, topic-based visual text summarization and analysis. In: Proceedings of the 16th SIGKDD international conference on knowledge discovery and data mining (KDD 2010), Washington, DC, pp. 153–162 (2010)

  42. Dörk, M., Gruen, D., Williamson, C., Carpendale, S.: A visual backchannel for large-scale events. IEEE Trans. Vis. Comput. Gr. 16(6), 1129–1138 (2010)

    Article  Google Scholar 

  43. Mohammad, S.M.: Challenges in sentiment analysis. In: Das, D., Cambria, E., Bandyopadhyay, S. (eds.) A Practical Guide to Sentiment Analysis, pp. 61–83. Springer, New York (2016)

    Google Scholar 

  44. World Health Organization: Prevention and control of dengue and dengue hemorrhagic fever: comprehensive guidelines. Technical report, World Health Organization Regional Office for South-East Asia (1999)

  45. Bhatt, S., Gething, P.W., Brady, O.J., Messina, J.P., Farlow, A.W., Moyes, C.L., Drake, J.M., Brownstein, J.S., Hoen, A.G., Sankoh, O.: The global distribution and burden of dengue. Nature 496(7446), 504 (2013)

    Article  Google Scholar 

  46. Montoya, M., Gresh, L., Mercado, J.C., Williams, K.L., Vargas, M.J., Gutierrez, G., Kuan, G., Gordon, A., Balmaseda, A., Harris, E.: Symptomatic versus inapparent outcome in repeat dengue virus infections is influenced by the time interval between infections and study year. PLoS Negl. Trop. Dis. 7(8), 2357 (2013)

    Article  Google Scholar 

  47. Moreira, L.A., Iturbe-Ormaetxe, I., Jeffery, J.A., Lu, G., Pyke, A.T., Hedges, L.M., Rocha, B.C., Hall-Mendelin, S., Day, A., Riegler, M.: A Wolbachia symbiont in Aedes Aegypti limits infection with dengue, chikungunya, and plasmodium. Cell 139(7), 1268–1278 (2009)

    Article  Google Scholar 

  48. Olkowski, S., Forshey, B.M., Morrison, A.C., Rocha, C., Vilcarromero, S., Halsey, E.S., Kochel, T.J., Scott, T.W., Stoddard, S.T.: Reduced risk of disease during postsecondary dengue virus infections. J. Infect. Dis. 208(6), 1026–1033 (2013)

    Article  Google Scholar 

  49. Reyes, M., Mercado, J.C., Standish, K., Matute, J.C., Ortega, O., Moraga, B., Avilés, W., Henn, M.R., Balmaseda, A., Kuan, G.: Index cluster study of dengue virus infection in Nicaragua. Am. J. Trop. Med. Hyg. 83(3), 683–689 (2010)

    Article  Google Scholar 

  50. Shepard, D.S., Undurraga, E.A., Halasa, Y.A.: Economic and disease burden of dengue in southeast asia. PLoS Negl. Trop. Dis. 7(2), 2055 (2013)

    Article  Google Scholar 

  51. Lozano, R., Naghavi, M., Foreman, K., Lim, S., Shibuya, K., Aboyans, V., Abraham, J., Adair, T., Aggarwal, R., Ahn, S.Y.: Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380(9859), 2095–2128 (2012)

    Article  Google Scholar 

  52. World Health Organization: Setting priorities in communicable disease surveillance. Technical report, World Health Organization, Lyon, France (2006)

  53. Brownstein, J.S., Freifeld, C.C., Reis, B.Y., Mandl, K.D.: Surveillance sans frontières: internet-based emerging infectious disease intelligence and the HealthMap project. PLoS Med. 5(7), 151 (2008)

    Article  Google Scholar 

  54. Davies, S.E.: The challenge to know and control: disease outbreak surveillance and alerts in China and India. Glob. Pub. Health 7(7), 695–716 (2012)

    Article  Google Scholar 

  55. Farrington, C.P., Andrews, N.J., Beale, A.D., Catchpole, M.A.: A statistical algorithm for the early detection of outbreaks of infectious disease. J. Royal Stat. Soc. Series A (Statistics in Society) 159(3), 547–563 (1996)

    Article  MATH  Google Scholar 

  56. Liu, Y.: China’s public health-care system: facing the challenges. Bull. World Health Organ. 82(7), 532–538 (2004)

    Google Scholar 

  57. Thacker, S.B., Qualters, J.R., Lee, L.M.: Public health surveillance in the United States: evolution and challenges. MMWR Surveill. Summ. 61, 3–9 (2012)

    Google Scholar 

  58. Beatty, M.E., Stone, A., Fitzsimons, D.W., Hanna, J.N., Lam, S.K., Vong, S., Guzman, M.G., Mendez-Galvan, J.F., Halstead, S.B., Letson, G.W.: Best practices in dengue surveillance: a report from the Asia-Pacific and Americas dengue prevention boards. PLoS Negl. Trop. Dis. 4(11), 890 (2010)

    Article  Google Scholar 

  59. Konowitz, P.M., Petrossian, G.A., Rose, D.N.: The underreporting of disease and physicians’ knowledge of reporting requirements. Pub. Health Rep. 99(1), 31 (1984)

    Google Scholar 

  60. McKenzie, J.F., Pinger, R.R.: An Introduction to Community Health, Brief Jones & Bartlett Publishers, Burlington (2013)

    Google Scholar 

  61. Singh, J., Dinkar, A., Atam, V., Himanshu, D., Gupta, K.K., Usman, K., Misra, R.: Awareness and outcome of changing trends in clinical profile of dengue fever: a retrospective analysis of dengue epidemic from January to December 2014 at a tertiary care hospital. J. Assoc. Phys. India 65, 42 (2017)

    Google Scholar 

  62. Fisher, R.A.: Statistical Methods for Research Workers. Oliver & Boyd, Edinburugh (1925)

    MATH  Google Scholar 

  63. Upton, G.J.: Fisher’s exact test. J. Royal Stat. Soc. Series A 155(3), 395–402 (1992)

    Article  Google Scholar 

  64. Kelly, J.T., Loepp, E.: Distinction without a difference? An assessment of MTurk worker types. Res. Polit. (2020). https://doi.org/10.11772/2053168019901185

    Article  Google Scholar 

  65. Sherlock, A.: Florence Nightingale’s “Rose” Diagram (2021). https://www.maharam.com/stories/sherlock_florence-nightingales-rose-diagram

  66. Villanes, A., Griffiths, E., Rappa, M., Healey, C.G.: Dengue fever surveillance in India using text mining in public media. Am. J. Trop. Med. Hyg. 98, 181–191 (2018)

    Article  Google Scholar 

  67. Agarwal, A., Fu, W., Menzies, T.: What is wrong with topic modeling? And how to fix it using search-based software engineering. Inf. Softw. Technol. 98, 74–88 (2018)

    Article  Google Scholar 

  68. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. 3(4–5), 993–1022 (2003)

    MATH  Google Scholar 

  69. Villanes, A.: Epidemiological disease surveillance using public media text mining. PhD thesis, North Carolina State University (2019)

Download references

Author information

Author notes

  1. Andrea Villanes and Christopher G. Healey have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science and Institute for Advanced Analytics, North Carolina State University, 890 Oval Drive, Raleigh, NC, 27695-8206, USA

    Andrea Villanes & Christopher G. Healey

Authors
  1. Andrea Villanes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christopher G. Healey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christopher G. Healey.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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

Villanes, A., Healey, C.G. Domain-specific text dictionaries for text analytics. Int J Data Sci Anal 15, 105–118 (2023). https://doi.org/10.1007/s41060-022-00344-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41060-022-00344-x

Keywords

Search

Navigation