This paper is in the following e-collection/theme issue:

New Methods (901) Peer-to-Peer Support and Online Communities (592) Natural Language Processing (636)

Published on in Vol 17, No 8 (2015): August

Automatically Detecting Failures in Natural Language Processing Tools for Online Community Text

Automatically Detecting Failures in Natural Language Processing Tools for Online Community Text

Automatically Detecting Failures in Natural Language Processing Tools for Online Community Text

Authors of this article:

Albert Park1 Author Orcid Image ;   Andrea L Hartzler2 Author Orcid Image ;   Jina Huh3 Author Orcid Image ;   David W McDonald4 Author Orcid Image ;   Wanda Pratt5 Author Orcid Image
Article Authors Cited by (28) Tweetations (26) Metrics

Journals

  1. Park A, Conway M, Chen A. Examining thematic similarity, difference, and membership in three online mental health communities from reddit: A text mining and visualization approach. Computers in Human Behavior 2018;78:98 View
  2. Chen D, Zhang R, Feng J, Liu K. Fulfilling information needs of patients in online health communities. Health Information & Libraries Journal 2020;37(1):48 View
  3. Dirkson A, Verberne S, Sarker A, Kraaij W. Data-Driven Lexical Normalization for Medical Social Media. Multimodal Technologies and Interaction 2019;3(3):60 View
  4. Hartzler A, Weis B, Cahill C, Pratt W, Park A, Backonja U, McDonald D. Design and Usability of Interactive User Profiles for Online Health Communities. ACM Transactions on Computer-Human Interaction 2016;23(3):1 View
  5. Zhang S, Bantum E, Owen J, Bakken S, Elhadad N. Online cancer communities as informatics intervention for social support: conceptualization, characterization, and impact. Journal of the American Medical Informatics Association 2017;24(2):451 View
  6. Weissman G, Ungar L, Harhay M, Courtright K, Halpern S. Construct validity of six sentiment analysis methods in the text of encounter notes of patients with critical illness. Journal of Biomedical Informatics 2019;89:114 View
  7. Park A, Conway M. Harnessing Reddit to Understand the Written-Communication Challenges Experienced by Individuals With Mental Health Disorders: Analysis of Texts From Mental Health Communities. Journal of Medical Internet Research 2018;20(4):e121 View
  8. Park A, Conway M. Longitudinal Changes in Psychological States in Online Health Community Members: Understanding the Long-Term Effects of Participating in an Online Depression Community. Journal of Medical Internet Research 2017;19(3):e71 View
  9. Sung S, Chen K, Wu D, Hung L, Su Y, Hu Y. Applying natural language processing techniques to develop a task-specific EMR interface for timely stroke thrombolysis: A feasibility study. International Journal of Medical Informatics 2018;112:149 View
  10. Chiaramello E, Pinciroli F, Bonalumi A, Caroli A, Tognola G. Use of “off-the-shelf” information extraction algorithms in clinical informatics: A feasibility study of MetaMap annotation of Italian medical notes. Journal of Biomedical Informatics 2016;63:22 View
  11. Guetterman T, Chang T, DeJonckheere M, Basu T, Scruggs E, Vydiswaran V. Augmenting Qualitative Text Analysis with Natural Language Processing: Methodological Study. Journal of Medical Internet Research 2018;20(6):e231 View
  12. Wang H, Zeng D. Fusing Logical Relationship Information of Text in Neural Network for Text Classification. Mathematical Problems in Engineering 2020;2020:1 View
  13. Hartzler A, Taylor M, Park A, Griffiths T, Backonja U, McDonald D, Wahbeh S, Brown C, Pratt W. Leveraging cues from person-generated health data for peer matching in online communities. Journal of the American Medical Informatics Association 2016;23(3):496 View
  14. Rudra K, Sharma A, Ganguly N, Imran M. Classifying and Summarizing Information from Microblogs During Epidemics. Information Systems Frontiers 2018;20(5):933 View
  15. VanDam C, Kanthawala S, Pratt W, Chai J, Huh J. Detecting clinically related content in online patient posts. Journal of Biomedical Informatics 2017;75:96 View
  16. Hegde H, Shimpi N, Glurich I, Acharya A. Tobacco use status from clinical notes using Natural Language Processing and rule based algorithm. Technology and Health Care 2018;26(3):445 View
  17. Park A. Tweets Related to Motivation and Physical Activity for Obesity-Related Behavior Change: Descriptive Analysis. Journal of Medical Internet Research 2022;24(7):e15055 View
  18. Lauer C, Brumberger E, Beveridge A. Hand Collecting and Coding Versus Data-Driven Methods in Technical and Professional Communication Research. IEEE Transactions on Professional Communication 2018;61(4):389 View
  19. Shoda V. Let’s play videos in literacy practice: From let’s play to let’s learn. E-Learning and Digital Media 2022;19(5):515 View
  20. Durieux B, Zverev S, Tarbi E, Kwok A, Sciacca K, Pollak K, Tulsky J, Lindvall C. Development of a keyword library for capturing PRO-CTCAE-focused “symptom talk” in oncology conversations. JAMIA Open 2023;6(1) View
  21. Oduru T, Jordan A, Park A. Healthy vs. Unhealthy Food Images: Image Classification of Twitter Images. International Journal of Environmental Research and Public Health 2022;19(2):923 View
  22. Shah A, Lee K, Hidayat A, Falchook A, Muhammad W. A text analytics approach for mining public discussions in online cancer forum: Analysis of multi-intent lung cancer treatment dataset. International Journal of Medical Informatics 2024;184:105375 View
  23. Malashin I, Masich I, Tynchenko V, Gantimurov A, Nelyub V, Borodulin A. Image Text Extraction and Natural Language Processing of Unstructured Data from Medical Reports. Machine Learning and Knowledge Extraction 2024;6(2):1361 View