An Evaluation of the Maternal Patient Experience through Natural Language Processing Techniques: The Case of Twitter Data in the United States during COVID-19
Abstract
:1. Introduction
- Utilize the NLP algorithm to evaluate patient experience related to maternal health using social media data;
- Classify text data into various topics relevant to maternal health and conduct sentimental analysis.
2. Literature Review
2.1. Research in Patient-Centered Care and Patient Experience Improvement
2.2. Research in Text Analytics
2.2.1. Topic Modeling
2.2.2. Sentiment Analysis
3. Methodology
3.1. Data Preprocessing
3.2. NLP Pipeline
3.2.1. Topic Modeling
- Randomly select a distribution over topics for every tweet;
- For every word in the tweet, carry out the following steps:
- Randomly select a topic from a distribution over topics in step 1;
- From the corresponding distribution over the vocabulary, randomly select a word.
3.2.2. Sentiment Analysis
3.2.3. N-Gram Analysis
4. Results
4.1. Preliminary Analysis of Disparity
4.2. Limitations of This Study
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Publications | Sentiment Analysis | Topic Modeling Using Latent Dirichlet Allocation (LDA) | Limitations | ||
---|---|---|---|---|---|
ML Approach | Lexicon-Based Approach | Online Reviews or Social Media | Electronic Health Record | ||
Patra et al. (2021) [74] | × | ||||
Li et al. (2022) [75] | × | Limited data as it used electronic healthcare records | |||
Fairie et al. (2021) [78] | × | Patient response is not directly collected from patients | |||
Torres-Silva et al. (2023) [113] | × | × | Lack of data accuracy | ||
Ortega (2021) [22] | × | × | Limited scope of study | ||
Okon et al. (2020) [71] | × | Output of the LDA model was not verified using coherence score | |||
Chintalapudi et al. (2021) [92] | × | × | Number of topics were not evaluated | ||
Zhong et al. (2019) [114] | × | × | Codified data | ||
Bartal et al. (2023) [115] | × | × | Data are not directly collected from patients | ||
Clapp et al. (2022) [116] | × | × | Data only collected from 2 hospitals | ||
Elbagir and Yang (2019) [95] | × | × | Small sample of dataset | ||
Kumar et al. (2020) [96] | × | × | Limited dataset |
Dominant Topic | Topic_Perc_Contrib | Actual Tweet | Polarity Vader | Subjectivity |
---|---|---|---|---|
2 | 0.7592 | @BMaienschein Thank you for seeing San Diego’s ME/CFS patients and our many community volunteers and supporters. At this point, we have donated 1620 masks, caps, and other PPE mostly to San Diego healthcare workers and organizations. https://t.co/CwaTq2TqwW #MECFSSD #aCure4ME https://t.co/6k54mUBPwr | 0.6597 | 0 |
2 | 0.5576 | East Texas doctors discuss possible pandemic baby boom: https://t.co/00LA8Ny7Tu https://t.co/sTDYNLqwWJ | 0 | 1 |
3 | 0.5631 | @realDonaldTrump @CDCgov COVID kills! This is not fake news people are dying. My brother lost his wife to COVID, mother of 7. Really Trump… | −0.5481 | 0 |
1 | 0.879 | Can’t wait for COVID to be over. My baby wanna go to NYC so bad I can’t wait to get her there | −0.6696 | 0.6666 |
Dominant topic | Average Polarity | Average Subjectivity |
---|---|---|
1 | −0.003856237 | 0.3548346 |
2 | 0.065616866 | 0.3669842 |
3 | −0.199848470 | 0.3639693 |
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Banik, D.; Chalil Madathil, S.; Lopes, A.J.; Luna Fong, S.A.; Mukka, S.K. An Evaluation of the Maternal Patient Experience through Natural Language Processing Techniques: The Case of Twitter Data in the United States during COVID-19. Appl. Sci. 2024, 14, 8762. https://doi.org/10.3390/app14198762
Banik D, Chalil Madathil S, Lopes AJ, Luna Fong SA, Mukka SK. An Evaluation of the Maternal Patient Experience through Natural Language Processing Techniques: The Case of Twitter Data in the United States during COVID-19. Applied Sciences. 2024; 14(19):8762. https://doi.org/10.3390/app14198762
Chicago/Turabian StyleBanik, Debapriya, Sreenath Chalil Madathil, Amit Joe Lopes, Sergio A. Luna Fong, and Santosh K. Mukka. 2024. "An Evaluation of the Maternal Patient Experience through Natural Language Processing Techniques: The Case of Twitter Data in the United States during COVID-19" Applied Sciences 14, no. 19: 8762. https://doi.org/10.3390/app14198762