scholarly journals Sentiment Analysis of Health Care Tweets: Review of the Methods Used (Preprint)

2016 ◽  
Author(s):  
Sunir Gohil ◽  
Sabine Vuik ◽  
Ara Darzi
Keyword(s):  
Health Care ◽  
Sentiment Analysis ◽  
Open Source ◽  
Care Setting ◽  
Health Care Research ◽  
Text Messages ◽  
Health Care Setting ◽  
Future Research ◽  
Free Text ◽  
Care Research

BACKGROUND Twitter is a microblogging service where users can send and read short 140-character messages called “tweets.” There are several unstructured, free-text tweets relating to health care being shared on Twitter, which is becoming a popular area for health care research. Sentiment is a metric commonly used to investigate the positive or negative opinion within these messages. Exploring the methods used for sentiment analysis in Twitter health care research may allow us to better understand the options available for future research in this growing field. OBJECTIVE The first objective of this study was to understand which tools would be available for sentiment analysis of Twitter health care research, by reviewing existing studies in this area and the methods they used. The second objective was to determine which method would work best in the health care settings, by analyzing how the methods were used to answer specific health care questions, their production, and how their accuracy was analyzed. METHODS A review of the literature was conducted pertaining to Twitter and health care research, which used a quantitative method of sentiment analysis for the free-text messages (tweets). The study compared the types of tools used in each case and examined methods for tool production, tool training, and analysis of accuracy. RESULTS A total of 12 papers studying the quantitative measurement of sentiment in the health care setting were found. More than half of these studies produced tools specifically for their research, 4 used open source tools available freely, and 2 used commercially available software. Moreover, 4 out of the 12 tools were trained using a smaller sample of the study’s final data. The sentiment method was trained against, on an average, 0.45% (2816/627,024) of the total sample data. One of the 12 papers commented on the analysis of accuracy of the tool used. CONCLUSIONS Multiple methods are used for sentiment analysis of tweets in the health care setting. These range from self-produced basic categorizations to more complex and expensive commercial software. The open source and commercial methods are developed on product reviews and generic social media messages. None of these methods have been extensively tested against a corpus of health care messages to check their accuracy. This study suggests that there is a need for an accurate and tested tool for sentiment analysis of tweets trained using a health care setting–specific corpus of manually annotated tweets first.

Communication in the Operating Room Setting

2017 ◽  
Vol 35 (1) ◽  
pp. 55-69 ◽  
Author(s):  
Lisa Osborne-Smith ◽  
R. Kyle Hodgen
Keyword(s):  
Health Care ◽  
Patient Care ◽  
Care Setting ◽  
Care Delivery ◽  
Team Building ◽  
Critical Time ◽  
Effective Communication ◽  
Health Care Setting ◽  
Future Research ◽  
Patient Care Delivery

Ideal and effective communication consists of a clear, audible, and focused message from a transmitter that is delivered to an attentive, undistracted receiver, and consists of both verbal and nonverbal types. Communication in the health care setting is highly complex and dynamic, involving multiple settings, participants, and unique challenges. Effective communication in the perioperative environment is a requirement for safe patient care delivery and an important element of teamwork. A message must be accurately delivered in a uniquely high-risk and time-sensitive location, beset with numerous distractions, barriers, and challenges. Surgical checklists and time-out procedures have promoted a standardized, "all-hands" approach to addressing some of the challenges to effective communication in the perioperative environment. Postoperative debriefing sessions have demonstrated effectiveness in improving team functioning in the simulated learning environment and hold promise as another strategy to address these challenges, but require further research and development. Other promising strategies to improve effective perioperative communication are focused on team building activities and minimizing distractions at critical time points within patient care delivery, but to date are not substantiated by evidence. Future research is necessary to examine these novel approaches to improving communication in the perioperative environment to influence the safety of patient care delivery in this highly challenging health care setting.

scholarly journals Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

2021 ◽  
Vol 18 (12) ◽  
pp. 6408
Author(s):  
Mario Jojoa Acosta ◽  
Gema Castillo-Sánchez ◽  
Begonya Garcia-Zapirain ◽  
Isabel de la Torre Díez ◽  
Manuel Franco-Martín
Keyword(s):  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Language Processing ◽  
Health Care Professionals ◽  
Ground Truth ◽  
Relevant Information ◽  
Free Text

The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.

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