Emergency care and the patient experience: Using sentiment analysis and topic modeling to understand the impact of the COVID-19 pandemic

BACKGROUND Social media is being used by various stakeholders among pharmaceutical companies, government agencies, health care organizations, professionals, and news media as a way of engaging audiences to raise disease awareness and ultimately to improve public health. Nevertheless, it is unclear what effects this health information has on laypeople. OBJECTIVE This study aimed to provide a detailed examination of how promotional health information related to Lynch syndrome impacts laypeople’s discussions on a social media platform (Twitter) in terms of topic awareness and attitudes. METHODS We used topic modeling and sentiment analysis techniques on Lynch syndrome–related tweets to answer the following research questions (RQs): (1) what are the most discussed topics in Lynch syndrome–related tweets?; (2) how promotional Lynch syndrome–related information on Twitter affects laypeople’s discussions?; and (3) what impact do the Lynch syndrome awareness activities in the Colon Cancer Awareness Month and Lynch Syndrome Awareness Day have on laypeople’s discussions and their attitudes? In particular, we used a set of keywords to collect Lynch syndrome–related tweets from October 26, 2016 to August 11, 2017 (289 days) through the Twitter public search application programming interface (API). We experimented with two different classification methods to categorize tweets into the following three classes: (1) irrelevant, (2) promotional health information, and (3) laypeople’s discussions. We applied a topic modeling method to discover the themes in these Lynch syndrome–related tweets and conducted sentiment analysis on each layperson’s tweet to gauge the writer’s attitude (ie, positive, negative, and neutral) toward Lynch syndrome. The topic modeling and sentiment analysis results were elaborated to answer the three RQs. RESULTS Of all tweets (N=16,667), 87.38% (14,564/16,667) were related to Lynch syndrome. Of the Lynch syndrome–related tweets, 81.43% (11,860/14,564) were classified as promotional and 18.57% (2704/14,564) were classified as laypeople’s discussions. The most discussed themes were treatment (n=4080) and genetic testing (n=3073). We found that the topic distributions in laypeople’s discussions were similar to the distributions in promotional Lynch syndrome–related information. Furthermore, most people had a positive attitude when discussing Lynch syndrome. The proportion of negative tweets was 3.51%. Within each topic, treatment (16.67%) and genetic testing (5.60%) had more negative tweets compared with other topics. When comparing monthly trends, laypeople’s discussions had a strong correlation with promotional Lynch syndrome–related information on awareness (r=.98, P<.001), while there were moderate correlations on screening (r=.602, P=.05), genetic testing (r=.624, P=.04), treatment (r=.69, P=.02), and risk (r=.66, P=.03). We also discovered that the Colon Cancer Awareness Month (March 2017) and the Lynch Syndrome Awareness Day (March 22, 2017) had significant positive impacts on laypeople’s discussions and their attitudes. CONCLUSIONS There is evidence that participative social media platforms, namely Twitter, offer unique opportunities to inform cancer communication surveillance and to explore the mechanisms by which these new communication media affect individual health behavior and population health.

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Online Product Reviews and Their Impact on Third Party Sellers Using Natural Language Processing

International Journal of Business Intelligence Research ◽

10.4018/ijbir.20210101.oa2 ◽

2021 ◽

Vol 12 (1) ◽

pp. 26-47

Author(s):

Akash Phaniteja Nellutla ◽

Manoj Hudnurkar ◽

Suhas Suresh Ambekar ◽

Abhay D. Lidbe

Keyword(s):

Sentiment Analysis ◽

Language Processing ◽

Topic Modeling ◽

Online Reviews ◽

Third Party ◽

Bag Of Words ◽

Product Reviews ◽

Specific Product ◽

Online Product Reviews ◽

The Impact

The purpose of this paper is to gain insights from the online product reviews of e-commerce sites such as Flipkart and Amazon and analyze its impact on third party sellers. To judge the authenticity of a product, reviews are more useful than ratings, since ratings do not give a complete picture. It is always preferred to consider both the product and seller reviews to have a seamless delivery and defect less product. In this paper, natural processing methods are used to gain insights by considering online reviews of a product. Methods such as sentiment analysis, bag of words model help to understand the impact of online product reviews on the seller's ratings and their performance over some time. The reviews are categorized into positive, negative, and neutral using sentiment analysis. Further, topic modeling is done to find out the topic reviews are majorly referring to. The seller reviews for a specific product after analysis are compared with the overall seller reviews to judge the authenticity. The results of this paper would be beneficial to both the consumers and sellers.

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Comparing News Articles and Tweets About COVID-19 in Brazil: Sentiment Analysis and Topic Modeling Approach

JMIR Public Health and Surveillance ◽

10.2196/24585 ◽

2021 ◽

Vol 7 (2) ◽

pp. e24585

Author(s):

Tiago de Melo ◽

Carlos M S Figueiredo

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

News Media ◽

Topic Modeling ◽

Entity Recognition ◽

Political Polarization ◽

Important Approach ◽

Topic Sentiment ◽

The Impact ◽

Over Time

Background The COVID-19 pandemic is severely affecting people worldwide. Currently, an important approach to understand this phenomenon and its impact on the lives of people consists of monitoring social networks and news on the internet. Objective The purpose of this study is to present a methodology to capture the main subjects and themes under discussion in news media and social media and to apply this methodology to analyze the impact of the COVID-19 pandemic in Brazil. Methods This work proposes a methodology based on topic modeling, namely entity recognition, and sentiment analysis of texts to compare Twitter posts and news, followed by visualization of the evolution and impact of the COVID-19 pandemic. We focused our analysis on Brazil, an important epicenter of the pandemic; therefore, we faced the challenge of addressing Brazilian Portuguese texts. Results In this work, we collected and analyzed 18,413 articles from news media and 1,597,934 tweets posted by 1,299,084 users in Brazil. The results show that the proposed methodology improved the topic sentiment analysis over time, enabling better monitoring of internet media. Additionally, with this tool, we extracted some interesting insights about the evolution of the COVID-19 pandemic in Brazil. For instance, we found that Twitter presented similar topic coverage to news media; the main entities were similar, but they differed in theme distribution and entity diversity. Moreover, some aspects represented negative sentiment toward political themes in both media, and a high incidence of mentions of a specific drug denoted high political polarization during the pandemic. Conclusions This study identified the main themes under discussion in both news and social media and how their sentiments evolved over time. It is possible to understand the major concerns of the public during the pandemic, and all the obtained information is thus useful for decision-making by authorities.

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38 Transforming post triage assessment of chest pain in a busy emergency department (ED), from ED doctor to advanced nurse practitioner (ANP) direct; the impact on patient experience times (PET)

10.1136/heartjnl-2020-ics.38 ◽

2020 ◽

Author(s):

S Ingram

Keyword(s):

Emergency Department ◽

Chest Pain ◽

Patient Experience ◽

Nurse Practitioner ◽

The Impact

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The patient advisor, an organizational resource as a lever for an enhanced oncology patient experience (PAROLE-onco): a longitudinal multiple case study protocol

BMC Health Services Research ◽

10.1186/s12913-020-06009-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

M. P. Pomey ◽

M. de Guise ◽

M. Desforges ◽

K. Bouchard ◽

C. Vialaron ◽

...

Keyword(s):

Cancer Patients ◽

Clinical Oncology ◽

Patient Experience ◽

Emotional Support ◽

Social Care ◽

Legal Issues ◽

Care Services ◽

Health And Social Care ◽

Ethical And Legal Issues ◽

The Impact

Abstract Background Quebec is one of the Canadian provinces with the highest rates of cancer incidence and prevalence. A study by the Rossy Cancer Network (RCN) of McGill university assessed six aspects of the patient experience among cancer patients and found that emotional support is the aspect most lacking. To improve this support, trained patient advisors (PAs) can be included as full-fledged members of the healthcare team, given that PA can rely on their knowledge with experiencing the disease and from using health and social care services to accompany cancer patients, they could help to round out the health and social care services offer in oncology. However, the feasibility of integrating PAs in clinical oncology teams has not been studied. In this multisite study, we will explore how to integrate PAs in clinical oncology teams and, under what conditions this can be successfully done. We aim to better understand effects of this PA intervention on patients, on the PAs themselves, the health and social care team, the administrators, and on the organization of services and to identify associated ethical and legal issues. Methods/design We will conduct six mixed methods longitudinal case studies. Qualitative data will be used to study the integration of the PAs into clinical oncology teams and to identify the factors that are facilitators and inhibitors of the process, the associated ethical and legal issues, and the challenges that the PAs experience. Quantitative data will be used to assess effects on patients, PAs and team members, if any, of the PA intervention. The results will be used to support oncology programs in the integration of PAs into their healthcare teams and to design a future randomized pragmatic trial to evaluate the impact of PAs as full-fledged members of clinical oncology teams on cancer patients’ experience of emotional support throughout their care trajectory. Discussion This study will be the first to integrate PAs as full-fledged members of the clinical oncology team and to assess possible clinical and organizational level effects. Given the unique role of PAs, this study will complement the body of research on peer support and patient navigation. An additional innovative aspect of this study will be consideration of the ethical and legal issues at stake and how to address them in the health care organizations.

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The Impact of Arabic Part of Speech Tagging on Sentiment Analysis: A New Corpus and Deep Learning Approach

Procedia Computer Science ◽

10.1016/j.procs.2021.03.026 ◽

2021 ◽

Vol 184 ◽

pp. 148-155

Author(s):

Abdul Munem Nerabie ◽

Manar AlKhatib ◽

Sujith Samuel Mathew ◽

May El Barachi ◽

Farhad Oroumchian

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Learning Approach ◽

Part Of Speech Tagging ◽

Part Of Speech ◽

The Impact ◽

Speech Tagging

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Association of Emergency Department Waiting Times With Patient Experience in Admitted and Discharged Patients

Journal of Patient Experience ◽

10.1177/23743735211011404 ◽

2021 ◽

Vol 8 ◽

pp. 237437352110114

Author(s):

Andrew Nyce ◽

Snehal Gandhi ◽

Brian Freeze ◽

Joshua Bosire ◽

Terry Ricca ◽

...

Keyword(s):

Emergency Department ◽

Length Of Stay ◽

Patient Experience ◽

Waiting Times ◽

Hospital Length ◽

Short Length ◽

Hospital Length Of Stay ◽

Patient’S Experience ◽

Discharged Patients ◽

The Impact

Prolonged waiting times are associated with worse patient experience in patients discharged from the emergency department (ED). However, it is unclear which component of the waiting times is most impactful to the patient experience and the impact on hospitalized patients. We performed a retrospective analysis of ED patients between July 2018 and March 30, 2020. In all, 3278 patients were included: 1477 patients were discharged from the ED, and 1680 were admitted. Discharged patients had a longer door-to-first provider and door-to-doctor time, but a shorter doctor-to-disposition, disposition-to-departure, and total ED time when compared to admitted patients. Some, but not all, components of waiting times were significantly higher in patients with suboptimal experience (<100th percentile). Prolonged door-to-doctor time was significantly associated with worse patient experience in discharged patients and in patients with hospital length of stay ≤4 days. Prolonged ED waiting times were significantly associated with worse patient experience in patients who were discharged from the ED and in inpatients with short length of stay. Door-to-doctor time seems to have the highest impact on the patient’s experience of these 2 groups.

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Analyzing Brexit’s impact using sentiment analysis and topic modeling on Twitter discussion

The 21st Annual International Conference on Digital Government Research ◽

10.1145/3396956.3396973 ◽

2020 ◽

Author(s):

Sardar Haider Waseem Ilyas ◽

Zainab Tariq Soomro ◽

Ahmed Anwar ◽

Hamza Shahzad ◽

Ussama Yaqub

Keyword(s):

Sentiment Analysis ◽

Topic Modeling

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Understanding the impact of a new pharmacy sore throat test and treat service on patient experience: A survey study

Research in Social and Administrative Pharmacy ◽

10.1016/j.sapharm.2020.07.034 ◽

2020 ◽

Author(s):

Efi Mantzourani ◽

Rebecca Cannings-John ◽

Andrew Evans ◽

Haroon Ahmed ◽

Alan Meudell ◽

...

Keyword(s):

Sore Throat ◽

Patient Experience ◽

Survey Study ◽

Test And Treat ◽

The Impact

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Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning

Electronics ◽

10.3390/electronics9020374 ◽

2020 ◽

Vol 9 (2) ◽

pp. 374 ◽

Cited By ~ 2

Author(s):

Sudhanshu Kumar ◽

Monika Gahalawat ◽

Partha Pratim Roy ◽

Debi Prosad Dogra ◽

Byung-Gyu Kim

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Short Term Memory ◽

Age Groups ◽

Modern World ◽

Support Vector ◽

Digital Information ◽

Age And Gender ◽

And Gender ◽

The Impact

Sentiment analysis is a rapidly growing field of research due to the explosive growth in digital information. In the modern world of artificial intelligence, sentiment analysis is one of the essential tools to extract emotion information from massive data. Sentiment analysis is applied to a variety of user data from customer reviews to social network posts. To the best of our knowledge, there is less work on sentiment analysis based on the categorization of users by demographics. Demographics play an important role in deciding the marketing strategies for different products. In this study, we explore the impact of age and gender in sentiment analysis, as this can help e-commerce retailers to market their products based on specific demographics. The dataset is created by collecting reviews on books from Facebook users by asking them to answer a questionnaire containing questions about their preferences in books, along with their age groups and gender information. Next, the paper analyzes the segmented data for sentiments based on each age group and gender. Finally, sentiment analysis is done using different Machine Learning (ML) approaches including maximum entropy, support vector machine, convolutional neural network, and long short term memory to study the impact of age and gender on user reviews. Experiments have been conducted to identify new insights into the effect of age and gender for sentiment analysis.

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