Abstract

The widespread use of social media provides a large amount of data for public sentiment analysis. Based on social media data, researchers can study public opinions on human papillomavirus (HPV) vaccines on social media using machine learning-based approaches that will help us understand the reasons behind the low vaccine coverage. However, social media data is usually unannotated, and data annotation is costly. The lack of an abundant annotated dataset limits the application of deep learning methods in effectively training models. To tackle this problem, we propose three transfer learning approaches to analyze the public sentiment on HPV vaccines on Twitter. One was transferring static embeddings and embeddings from language models (ELMo) and then processing by bidirectional gated recurrent unit with attention (BiGRU-Att), called DWE-BiGRU-Att. The others were fine-tuning pre-trained models with limited annotated data, called fine-tuning generative pre-training (GPT) and fine-tuning bidirectional encoder representations from transformers (BERT). The fine-tuned GPT model was built on the pre-trained generative pre-training (GPT) model. The fine-tuned BERT model was constructed with BERT model. The experimental results on the HPV dataset demonstrated the efficacy of the three methods in the sentiment analysis of the HPV vaccination task. The experimental results on the HPV dataset demonstrated the efficacy of the methods in the sentiment analysis of the HPV vaccination task. The fine-tuned BERT model outperforms all other methods. It can help to find strategies to improve vaccine uptake.

Highlights

  • IntroductionWith the rapid development of social media, the public can share their emotion, opinion, medical experience, and professional knowledge on public health issues such as infectious disease prevention [1,2], drug safety supervision [3,4], health promotion [5,6,7], and vaccination [8,9,10,11]

  • With the rapid development of social media, the public can share their emotion, opinion, medical experience, and professional knowledge on public health issues such as infectious disease prevention [1,2], drug safety supervision [3,4], health promotion [5,6,7], and vaccination [8,9,10,11].Human papillomavirus (HPV) is the most widespread sexually transmitted infection (STI) around the world

  • The columns of BOW-BiGRU-Att, Word2Vec-BiGRU-Att, FastText-BiGRU-Att, GloVe-BiGRU-Att, and embeddings from language models (ELMo)-BiGRU-Att are our experiment results of bidirectional long short-term memory combined with bag-of-word, Word2Vec, pre-trained FastTest embedding, GloVe embedding, and ELMo embedding respectively

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Summary

Introduction

With the rapid development of social media, the public can share their emotion, opinion, medical experience, and professional knowledge on public health issues such as infectious disease prevention [1,2], drug safety supervision [3,4], health promotion [5,6,7], and vaccination [8,9,10,11]. Human papillomavirus (HPV) is the most widespread sexually transmitted infection (STI) around the world. It has been established that approximately 4% of all cancers are associated with HPV [1]. HPV vaccines can prevent most cancers and diseases caused by HPV infections [8]. Despite the recommendation about the vaccine’s safety and effect, HPV vaccination rates in many countries are still far lower than the goal set by Healthy People 2020 of 80% series completion for both adolescent males and females [9]. Du et al [10] collected and manually

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