Shot Or Not: Comparison of NLP Approaches for Vaccination Behaviour Detection

Vaccination behaviour detection deals with predicting whether or not a person received/was about to receive a vaccine. We present our submission for vaccination behaviour detection shared task at the SMM4H workshop. Our findings are based on three prevalent text classification approaches: rule-based, statistical and deep learning-based. Our final submissions are: (1) an ensemble of statistical classifiers with task-specific features derived using lexicons, language processing tools and word embeddings; and, (2) a LSTM classifier with pre-trained language models.

[1]  Steven Bird,et al.  NLTK: The Natural Language Toolkit , 2002, ACL.

[2]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[3]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[4]  Christopher Potts,et al.  Learning Word Vectors for Sentiment Analysis , 2011, ACL.

[5]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[6]  Erik Cambria,et al.  SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis , 2014, AAAI.

[7]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[8]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[9]  S. Kochhar,et al.  The importance of the patient voice in vaccination and vaccine safety-are we listening? , 2016, Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases.

[10]  Mark Dredze,et al.  Examining Patterns of Influenza Vaccination in Social Media , 2017, AAAI Workshops.

[11]  Andreas Kerren,et al.  Automatic detection of stance towards vaccination in online discussion forums , 2017, DDDSM@IJCNLP.

[12]  Richard Socher,et al.  Pointer Sentinel Mixture Models , 2016, ICLR.

[13]  Sebastian Ruder,et al.  Universal Language Model Fine-tuning for Text Classification , 2018, ACL.