Sentiment analysis of tweets on prior authorization
| Autor: | Syed Mohammed Qasim Hussaini, Naveen Premnath, Pamela T. Johnson, Shivika Prasanna, Rohan Khera, Muhammad Shaalan Beg, Praveen Rao, Suveen Angraal, Helen Parsons, Ishwaria Mohan Subbiah, Arjun Gupta, Ramy Sedhom, Emil Lou |
|---|---|
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Journal of Clinical Oncology. 39:322-322 |
| ISSN: | 1527-7755 0732-183X |
| DOI: | 10.1200/jco.2020.39.28_suppl.322 |
| Popis: | 322 Background: Natural language processing (NLP) algorithms can be leveraged to better understand prevailing themes in healthcare conversations. Sentiment analysis, an NLP technique to analyze and interpret sentiments from text, has been validated on Twitter in tracking natural disasters and disease outbreaks. To establish its role in healthcare discourse, we sought to explore the feasibility and accuracy of sentiment analysis on Twitter posts (‘’tweets’’) related to prior authorizations (PAs), a common occurrence in oncology built to curb payer-concerns about costs of cancer care, but which can obstruct timely and appropriate care and increase administrative burden and clinician frustration. Methods: We identified tweets related to PAs between 03/09/2021-04/29/2021 using pre-specified keywords [e.g., #priorauth etc.] and used Twarc, a command-line tool and Python library for archiving Twitter JavaScript Object Notation data. We performed sentiment analysis using two NLP models: (1) TextBlob (trained on movie reviews); and (2) VADER (trained on social media). These models provide results as polarity, a score between 0-1, and a sentiment as ‘’positive’’ (>0), ‘’neutral’’ (exactly 0), or ‘’negative’’ ( |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|