Sentiment analysis in medication adherence: using ruled-based and artificial intelligence-driven algorithms to understand patient medication experiences.

BACKGROUND: Studies are exploring ways to improve medication adherence, with sentiment analysis (SA) being an underutilized innovation in pharmacy. This technique uses artificial intelligence (AI) and natural language processing to assess text for underlying feelings and emotions. AIM: This study aimed to evaluate the use of two SA models, Valence Aware Dictionary for Sentiment Reasoning (VADER) and Emotion English DistilRoBERTa-base (DistilRoBERTa), for the identification of patients' sentiments and emotions towards their pharmacotherapy. METHOD: A dataset containing 320,095 anonymized patients' reports of experiences with their medication was used. VADER assessed sentiment polarity on a scale from - 1 (negative) to + 1 (positive). DistilRoBERTa classified emotions into seven categories: anger, disgust, fear, joy, neutral, sadness, and surprise. Performance metrics for the models were obtained using the sklearn.metrics module of scikit-learn in Python. RESULTS: VADER demonstrated an overall accuracy of 0.70. For negative sentiments, it achieved a precision of 0.68, recall of 0.80, and an F1-score of 0.73, while for positive sentiments, it had a precision of 0.73, recall of 0.59, and an F1-score of 0.65. The AUC for the ROC curve was 0.90. DistilRoBERTa analysis showed that higher ratings for medication effectiveness, ease of use, and satisfaction corresponded with more positive emotional responses. These results were consistent with VADER's sentiment analysis, confirming the reliability of both models. CONCLUSION: VADER and DistilRoBERTa effectively analyzed patients' sentiments towards pharmacotherapy, providing valuable information. These findings encourage studies of SA in clinical pharmacy practice, paving the way for more personalized and effective patient care strategies.

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence.

From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment analysis of nanocomposite literature, distinguishing itself from existing reviews through its unique computational methodology. Developed by our research group, this novel approach systematically investigates the evolution of nanocomposites, focusing on microstructural characterization, electrical properties, and mechanical behaviors. By deploying advanced Boolean search strategies within the Scopus database, we achieve a meticulous extraction and in-depth exploration of thematic content, a methodological advancement in the field. Our analysis uniquely identifies critical trends and insights concerning nanocomposite microstructure, electrical attributes, and mechanical performance. The paper goes beyond traditional textual analytics and bibliometric evaluation, offering new interpretations of data and highlighting significant collaborative efforts and influential studies within the nanocomposite domain. Our findings uncover the evolution of research language, thematic shifts, and global contributions, providing a distinct and comprehensive view of the dynamic evolution of nanocomposite research. A critical component of this study is the "State-of-the-Art and Gaps Extracted from Results and Discussions" section, which delves into the latest advancements in nanocomposite research. This section details various nanocomposite types and their properties and introduces novel interpretations of their applications, especially in nanocomposite films. By tracing historical progress and identifying emerging trends, this analysis emphasizes the significance of collaboration and influential studies in molding the field. Moreover, the "Literature Review Guided by Artificial Intelligence" section showcases an innovative AI-guided approach to nanocomposite research, a first in this domain. Focusing on articles from 2023, selected based on citation frequency, this method offers a new perspective on the interplay between nanocomposites and their electrical properties. It highlights the composition, structure, and functionality of various systems, integrating recent findings for a comprehensive overview of current knowledge. The sentiment analysis, with an average score of 0.638771, reflects a positive trend in academic discourse and an increasing recognition of the potential of nanocomposites. Our bibliometric analysis, another methodological novelty, maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While acknowledging its limitations, this study exemplifies the indispensable role of our innovative computational tools in synthesizing and understanding the extensive body of nanocomposite literature. This work not only elucidates prevailing trends but also contributes a unique perspective and novel insights, enhancing our understanding of the nanocomposite research field.

Anxiety, emotions of the odontologist and measures adopted in clinical care during the COVID-19 pandemic / Ansiedad, sentimientos del odontólogo y medidas adoptadas en la atención clínica durante la pandemia COVID-19

Introducción: la pandemia COVID-19 ha afectado la práctica odontológica por el alto riesgo de contagio durante su ejercicio. El objetivo del estudio fue determinar el nivel de ansiedad, sentimientos manifestados y medidas adoptadas en la atención clínica por el odontólogo durante la pandemia COVID-19 en Lima Metropolitana-Perú. Métodos: estudio descriptivo, prospectivo y transversal realizado en 386 odontólogos de Lima Metropolitana-Perú en los primeros meses del 2021. El nivel de ansiedad se midió a través del: Generalized Anxiety Disorder. Se confeccionó y validó un cuestionario para medir los sentimientos y medidas adoptadas, que fue enviado a través de distintas redes sociales. Resultados: los niveles de ansiedad leve y moderada se presentaron en el 42,5% y 21,2% respectivamente, presentándose mayores niveles de severidad en las mujeres (p<0,001). A mayor edad y número de años de experiencia profesional el nivel de ansiedad fue menor (Rho=-0,132; p=0,009) y (Rho=-0,129, p=0,011). Los sentimientos experimentados aumentaron a medida que el nivel de ansiedad fue mayor p<0,001. El 97,7% manifestó haber modificado el uso de equipos de protección personal como el uso de mascarillas respiratorias tipo N95 y el protector facial, y el 48,7% reveló haber tenido alguna dificultad para obtenerlo. La medida más utilizada en la atención clínica fue el lavado de manos antes y después de cada atención (92,7%) y el uso de luz ultravioleta (42,5%) fue la menos utilizada. Conclusiones: los odontólogos han cambiado el protocolo de atención, manifestando diferentes niveles de ansiedad acompañados de sentimientos negativos durante la pandemia COVID-19.

Introduction: the COVID-19 pandemic has affected dental practice, due to the high risk of contagion during its practice. The objective of the study was to evaluate the level of anxiety; emotions and measures taken by dentists during COVID-19 pandemic in Lima Metropolitana-Peru. Methods: the study was descriptive, prospective, and cross-sectional carried out in 386 dentists practicing in Lima Metropolitan-Peru during the first months of 2021. Level of anxiety was assessed by "Generalized Anxiety Disorder 7-item" (GAD-7) scale. A questionnaire was developed and validated to evaluate emotions and measures taken and was sent through different social networks. Results: mild and moderate anxiety were present in 42.5% and 21.2% respectively, with higher levels of severity in women (p<0.001). The higher the age and number of years of professional experience, the level of anxiety was lower (Rho=-0.132; p=0.009) and (Rho=-0.129, p=0.011). The emotions increased as the level of anxiety was higher p<0.001. 97.7% stated that they had modified the use of personal protective equipment such as the use of N95-type respiratory masks and face shields, and 48.7% revealed that they had some difficulty in obtaining it. The most used measure in clinical practice was hand washing before and after treatment (92.7%), while the use of ultraviolet light (42.5%) was the least used. Conclusions: dentists have changed care protocol, manifesting different levels of anxiety accompanied by negative emotions during COVID-19 pandemic.

Sentiment Analysis toward the COVID-19 Vaccine in the Main Latin American Media on Twitter: The Cases of Argentina, Chile, Colombia, Mexico, and Peru.

This article analyzes the media coverage of the COVID-19 vaccine by major media outlets in five Latin American countries: Argentina, Colombia, Chile, Mexico, and Peru. For this purpose, the XLM-roBERTa model was applied and the sentiments of all tweets published between January 2020 and June 2023 (n = 24,243) by the five outlets with the greatest online reach in each country were analyzed. The results show that the sentiment in the overall media and in each nation studied was mostly negative, and only at the beginning of the pandemic was there some positivity. In recent months, negative sentiment has increased twelvefold over positive sentiment, and has also garnered many more interactions than positive sentiment. The differences by platform and country are minimal, but there are markedly negative media, some more inclined to neutrality, and only one where positive sentiment predominates. This paper questions the role of journalism in Latin America during a health crisis as serious as that of the coronavirus, in which, instead of the expected neutrality, or even a certain message of hope, the media seem to have been dragged along by the negativity promoted by certain discourses far removed from scientific evidence.

Volatility of the COVID-19 vaccine hesitancy: sentiment analysis conducted in Brazil.

Background: Vaccine hesitancy is a phenomenon that can interfere with the expansion of vaccination coverage and is positioned as one of the top 10 global health threats. Previous studies have explored factors that affect vaccine hesitancy, how it behaves in different locations, and the profile of individuals in which it is most present. However, few studies have analyzed the volatility of vaccine hesitancy. Objective: Identify the volatility of vaccine hesitancy manifested in social media. Methods: Twitter's academic application programming interface was used to retrieve all tweets in Brazilian Portuguese mentioning the COVID-19 vaccine in 3 months (October 2020, June 2021, and October 2021), retrieving 1,048,576 tweets. A sentiment analysis was performed using the Orange software with the lexicon Multilingual sentiment in Portuguese. Results: The feelings associated with vaccine hesitancy were volatile within 1 month, as well as throughout the vaccination process, being positioned as a resilient phenomenon. The themes that nurture vaccine hesitancy change dynamically and swiftly and are often associated with other topics that are also affecting society. Conclusion: People that manifest the vaccine hesitancy present arguments that vary in a short period of time, what demand that government strategies to mitigate vaccine hesitancy effects be agile and counteract the expressed fear, by presenting scientific arguments.

Ambient light exposure of photostimulable phosphor plates: is there a safe limit for acceptable image quality?

OBJECTIVES: To subjectively assess radiographs obtained with photostimulable phosphor (PSP) plates exposed to clinical levels of ambient light prior to read-out to potentially set a safe limit for acceptable image quality. METHODS AND MATERIALS: Six dental regions of a dry human skull were X-rayed using PSP plates from VistaScan and Express under four exposure times: 0.1, 0.2, 0.32, and 0.4 s. Before read-out, the PSP plates were exposed to ambient light for 0, 5, 10, 30, 60, and 90 s. Six observers were asked to classify the 288 resulting radiographs as acceptable or unacceptable based on the identification of anatomical structures and global image quality. The number of answers classifying radiographs as unacceptable was used to calculate a rejection rate; a pairwise comparison for better image quality was further conducted among radiographs considered acceptable. Reproducibility was tested by having 25% of all experimental groups reassessed. RESULTS: Intra- and interobserver agreement ranged from 0.87 to 1.00 and from 0.81 to 0.92, respectively. Exposure of PSP plates to ambient light increased rejection rates mostly as of 10 s. In the pairwise comparison, subtle differences were observed between radiographs obtained with PSP plates not exposed and those exposed to ambient light for 5 s. CONCLUSIONS: Ambient light exposure of PSP plates impairs the image quality of radiographs. A safe limit of ambient light exposure of 5 s for VistaScan and Express should be considered. Ambient light exposure of PSP plates within safe limits can avoid retakes and reduce unnecessary patient exposure to X-rays.

Deep Learning Model for COVID-19 Sentiment Analysis on Twitter.

The COVID-19 pandemic impacted the mood of the people, and this was evident on social networks. These common user publications are a source of information to measure the population's opinion on social phenomena. In particular, the Twitter network represents a resource of great value due to the amount of information, the geographical distribution of the publications and the openness to dispose of them. This work presents a study on the feelings of the population in Mexico during one of the waves that produced the most contagion and deaths in this country. A mixed, semi-supervised approach was used, with a lexical-based data labeling technique to later bring these data to a pre-trained model of Transformers completely in Spanish. Two Spanish-language models were trained by adding to the Transformers neural network the adjustment for the sentiment analysis task specifically on COVID-19. In addition, ten other multilanguage Transformer models including the Spanish language were trained with the same data set and parameters to compare their performance. In addition, other classifiers with the same data set were used for training and testing, such as Support Vector Machines, Naive Bayes, Logistic Regression, and Decision Trees. These performances were compared with the exclusive model in Spanish based on Transformers, which had higher precision. Finally, this model was used, developed exclusively based on the Spanish language, with new data, to measure the sentiment about COVID-19 of the Twitter community in Mexico.

Revisiting dynamic range and image enhancement ability of contemporary digital radiographic systems-part 2: a subjective assessment.

OBJECTIVE: To assess the subjective image quality of original and manually enhanced radiographs acquired at different X-ray exposure times and using different digital systems. METHODS: A total of 500 radiographs obtained under 10 exposure times, 5 digital systems, and 2 enhancement conditions were assessed by 5 observers, who were asked to categorize each radiograph into acceptable or unacceptable. A radiograph was considered to be acceptable when at least four out of five observers found it acceptable. Descriptive analysis was used to summarize the outcomes and compare the subjective image quality of original and manually enhanced digital radiographs among different X-ray exposure times and digital systems. RESULTS: Express had six exposure times producing acceptable original images within a range from 0.063 to 0.4 s, followed by Digora Toto, which had five within a range from 0.063 to 0.32 s, Digora Optime, which had four within a range from 0.063 to 0.2 s,and SnapShot and VistaScan, which had 2 (0.2 and 0.32 s) and 1 (0.63 s), respectively. Image enhancement turned unacceptable images into acceptable ones in four digital systems: SnapShot at three exposure times, Digora Toto at two exposure times, Express at one exposure time, and VistaScan at four exposure times. CONCLUSION: Image enhancement based on brightness and/or contrast adjustments may be necessary to reveal the useful dynamic range of some digital radiographic systems and PSP-based systems may not necessarily have a wider range than sensor-based systems.

Ansiedade, medo, angústia e tristeza: principais sentimentos relatados por homossexuais e bissexuais na Covid-19 / Anxiety, fear, anguish and sadness: main feelings reported by homosexuals and bisexuals in Covid-19

Introdução: O grupo LGBTQIAPN+, pela construção histórica, já sofre exclusão social, LGBTfobia, sentimentos de inaptidão social, dificuldades no acesso a serviços de saúde e conflitos dentro do próprio ambiente familiar. Agora, no contexto da pandemia, se faz necessária a adaptação às novas regras de convívio e solidão. Objetivo: Descrever os fatores sociodemográficos e os sentimentos dos homossexuais e bissexuais diante a pandemia de Covid-19.Método: Trata-se de um estudo transversal descritivo com abordagem quantitativa realizado entre junho e julho de 2020, através de um formulário digital, por meio das plataformas sociais com a população de homossexuais e bissexuais das cinco macrorregiões brasileiras. As variáveis quantitativas foram apresentadas em valores absolutos e percentuais, focalizando na variável "emoções a respeito da pandemia de Covid-19", através de uma nuvem de palavras. Resultados: Os participantes são do gênero feminino com idade média de 23 anos, bissexuais, da raça branca, com ensino superior completo e que residem predominantemente na região Sudeste. Os sentimentos mais citados foram ansiedade, medo, angústia e tristeza. Conclusão: O público de homossexuais e bissexuais não diferiram os sentimentos em relação à população em geral, mas acredita-se que tais sentimentos já eram vivenciados por essa população devido aos estigmas enfrentados e foram agravados.

Introduction: The LGBTQIAPN+ group, by historical construction, already suffers social exclusion, LGBTphobia, feelings of social inadequacy, difficulties in access to health services and conflicts within the family environment itself. Now, in the context of the pandemic, it is necessary to adapt to new rules of coexistence and loneliness. Objective: To describe the sociodemographic factors and feelings of homosexuals and bisexuals facing the covid-19 pandemic.Method: This is a descriptive cross-sectional study with a quantitative approach conducted between June and July 2020, through a digital form, by means of social platforms with the population of homosexuals and bisexuals in the five Brazilian macro-regions. The quantitative variables were presented in absolute values and percentages, focusing on the variable "emotions regarding the Covid-19 pandemic" through a word cloud. Results: The participants, are female with a middle age of 23 years, bisexual, of white race, with complete higher education and residing predominantly in the Southeast region. The most frequent feelings mentioned were anxiety, fear, anguish and sadness. Conclusion: The homosexual and bisexual public did not have different feelings in relation to the general population, but it is believed that such feelings were already experienced by this population due to the stigmas faced and were aggravated.

Sentiment analysis in tweets: an assessment study from classical to modern word representation models.

With the exponential growth of social media networks, such as Twitter, plenty of user-generated data emerge daily. The short texts published on Twitter - the tweets - have earned significant attention as a rich source of information to guide many decision-making processes. However, their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks, including sentiment analysis. Sentiment classification is tackled mainly by machine learning-based classifiers. The literature has adopted different types of word representation models to transform tweets to vector-based inputs to feed sentiment classifiers. The representations come from simple count-based methods, such as bag-of-words, to more sophisticated ones, such as BERTweet, built upon the trendy BERT architecture. Nevertheless, most studies mainly focus on evaluating those models using only a small number of datasets. Despite the progress made in recent years in language modeling, there is still a gap regarding a robust evaluation of induced embeddings applied to sentiment analysis on tweets. Furthermore, while fine-tuning the model from downstream tasks is prominent nowadays, less attention has been given to adjustments based on the specific linguistic style of the data. In this context, this study fulfills an assessment of existing neural language models in distinguishing the sentiment expressed in tweets, by using a rich collection of 22 datasets from distinct domains and five classification algorithms. The evaluation includes static and contextualized representations. Contexts are assembled from Transformer-based autoencoder models that are also adapted based on the masked language model task, using a plethora of strategies.

Sentimentos de estudantes de mestrado diante a morte da pessoa em situação crítica / Feelings of students in a master's in nursing program facing the death of patients in a critical situation

A morte faz parte do quotidiano da vida dos enfermeiros, principalmente quando lidam com pacientes em situação crítica. A forma como percecionam a morte pode ser relevante para uma prestação de cuidados humanizados. Objetivo: identificar quais os sentimentos de estudantes de um programa de mestrado perante a morte e a sua influência na prestação de cuidados a pacientes críticos. Método: Trata-se de uma pesquisa de abordagem qualitativa, aprovada pela comissão de ética da Universidade (Doc11/CE/2018 de 09/04/2018). Realizaram-se entrevistas a 11 estudantes, processadas por análise de conteúdo. Resultados: os principais sentimentos associados à morte dos pacientes foram: impotência, frustração/revolta, tristeza/angústia, aceitação, alívio e distanciamento. Conclusão: os estudantes criam barreiras emocionais para gerir o sofrimento dos pacientes com que lidam, sendo importante que estas não comprometam a qualidade dos cuidados. Os sentimentos dos estudantes perante a morte influenciam positivamente o cuidado ao paciente crítico, pois as vivências e experiência de vida permitem-lhes prestá-los com mais serenidade

Death is part of nurses' daily lives, especially when they deal with patients in critical situations. The way they perceive death may be relevant for providing humanized care. Objective: to identify the feelings of students of a master's program towards death and its influence on care provided to critical patients. Method: This is a study with a qualitative approach, approved by the ethics committee of the University (Doc11/CE/2018 of 09/04/2018). Interviews were carried out with 11 students, processed by content analysis. Results: the main feelings associated with the death of patients were: impotence, frustration/revolt, sadness/anguish, acceptance, relief, and distancing. Conclusion: students create emotional barriers to manage the suffering of the patients they deal with, and it is important that these do not compromise the quality of care. Students' feelings towards death positively influence critical patient care, as their experiences and life experience allow them to provide care more calmly.

Análisis de sentimientos y tópicos sobre las vacunas cubanas Soberana 02 y Abdala en Twitter

El análisis de sentimientos o minería de opiniones es una rama de la computación que permite analizar opiniones, sentimientos y emociones en ciertas áreas de interés social como productos, servicios, organizaciones, compañías, eventos y temas de interés actual. En tal sentido se propuso identificar los sentimientos y tópicos presentes en los tweets que hicieron mención a las vacunas cubanas Soberana 02 y Abdala en la red social Twitter. Se optó por los lenguajes de programación Python y R con sus librerías específicas para la ciencia de datos. La primera parte del estudio, que abarcó desde el web scraping hasta la cuantificación de las palabras más usadas, se realizó con Python y las siguientes librerías: tweepy, pandas, re, nltk y matplotlib. Mientras que la segunda, que fue la del análisis de sentimientos y detección de tópicos, se implementó con R y se utilizó: tokenizers, tm, syuzhet, topic modeling, tidyverse, barplot y wordcloud. Se obtuvo que entre los términos con que más se dialoga en Twitter están dosis, vacunas, eficacia, cubanos, candidatos, millones, país, personas, recibido y población. En los tweets las emociones predominantes fueron el miedo y, ligeramente por encima, la confianza; en la polaridad predominó la positiva, como expresión del contexto vivido en el cual se desarrolló la campaña de vacunación. A partir de los tópicos identificados y los términos que se relacionaron con las emociones predominantes, así como por la polaridad, se aprecia consenso en torno a las vacunas Soberana 02 y Abdala.

Sentiment analysis or opinion mining is a branch of computing that allows analyzing opinions, feelings and emotions in certain areas of social interest such as products, services, organizations, companies, events and topics of current interest. In this sense, the objective of this paper was to identify the feelings and topics present in the tweets mentioning the Cuban vaccines Soberana 02 and Abdala on Twitter social network. The programming languages Python and R with their specific libraries for data science were chosen. The first part of the study, which ranged from web scraping to the quantification of the most used words, was carried out with Python and the libraries tweepy, pandas, re, nltk and matplotlib. While the second, which was the sentiment analysis and topic detection, was implemented with R and used tokenizers, tm, syuzhet, topic modeling, tidyverse, barplot, and wordcloud. It was obtained that among the terms with which there is more dialogue on Twitter are doses, vaccines, efficacy, Cubans, candidates, millions, country, people, received and population. In the tweets, the predominant emotions were fear and confidence, slightly above it; in the polarity, the positive one predominated, as an expression of the lived context in which the vaccination campaign was developed. A consensus can be perceived around the vaccines Soberana 02 and Abdala, from the identified topics and the terms that were related to the predominant emotions, as well as the polarity.

Feelings about birth by a group of high-risk pregnant women / Sentimientos sobre el parto de un grupo de gestantes de alto riesgo / Sentimentos a respeito do nascimento por um grupo de gestantes de alto risco

ABSTRACT Objective: to understand feelings about birth among a group of high-risk pregnant women. Method: a descriptive and qualitative study, using Alfred Schütz's social phenomenology as a philosophical theoretical framework. The study included 25 pregnant women undergoing high-risk prenatal care. The interview had the following guiding questions: tell me about your feelings regarding the moment of birth/childbirth; How do you deal with the high-risk diagnosis? What are your expectations for birth/childbirth? Results: five categories emerged: Fear of obstetric care; Fear of complications with the baby; Fear of cesarean section; Resilience in the face of high-risk pregnancy; and Expectations for birth. Considerations: high-risk pregnant women are afraid of the care they will receive, the risks and concern about the baby's vitality at birth. The importance of care is emphasized, with a welcoming environment, bonding and communication between health team and pregnant woman.

RESUMEN Objetivo: comprender los sentimientos sobre el parto de un grupo de gestantes de alto riesgo. Método: estudio descriptivo y cualitativo, utilizando como marco teórico filosófico la fenomenología social de Alfred Schütz. El estudio incluyó a 25 mujeres embarazadas que se sometían a atención prenatal de alto riesgo. La entrevista tuvo las siguientes preguntas orientadoras: cuénteme sobre sus sentimientos con respecto al momento del nacimiento/parto; ¿Cómo lidia con el diagnóstico de alto riesgo? ¿Cuáles son sus expectativas para el nacimiento/parto? Resultados: surgieron cinco categorías: Miedo a la atención obstétrica; Miedo a las complicaciones con el bebé; Miedo a la cesárea; Resiliencia ante el embarazo de alto riesgo; y Expectativas de nacimiento. Consideraciones: las gestantes de alto riesgo tienen miedo de la asistencia que recibirán, de los riesgos y aprensión en cuanto a la vitalidad del bebé al nacer. Se destaca la importancia de la asistencia con ambiente acogedor, vínculo y comunicación entre el equipo de salud y la gestante.

RESUMO Objetivo: compreender os sentimentos a respeito do nascimento por um grupo de gestantes de alto risco. Método: estudo descritivo e qualitativo, tendo a fenomenologia social de Alfred Schütz como referencial teórico filosófico. Participaram do estudo 25 gestantes em acompanhamento de pré-natal de alto risco. A entrevista contou com as seguintes questões norteadoras: fale-me sobre seus sentimentos em relação ao momento do nascimento/parto; Como você lida com o diagnóstico de alto risco? Quais suas expectativas para o nascimento/parto? Resultados: emergiram cinco categorias: Medo da assistência obstétrica; Medo das complicações com o bebê; Medo da cesariana; A resiliência diante da gestação de alto risco; e Expectativas para o nascimento. Considerações: as gestantes de alto risco sentem medo da assistência que receberão, dos riscos e apreensividade quanto à vitalidade do bebê no nascimento. Ressalta-se a importância de assistência com ambiente acolhedor, efetivação de vínculo e comunicação entre equipe de saúde e gestante.

Distanciamento social na pandemia da covid-19: sentimentos vivenciados por pessoas idosas / Distanciamiento social en la pandemia de covid-19: sentimientos experimentados por pesronas mayores / Social distancing in the covid-19 pandemic: feelings experienced by older people

Objetivo: conhecer os sentimentos vivenciados por pessoas idosas diante do distanciamento social na pandemia da Covid-19. Método: estudo qualitativo que adotou como referencial a Teoria das Representações Sociais pelo método do Discurso do Sujeito Coletivo. Participaram do estudo 29 pessoas idosas e a seleção foi do tipo intencional ou teórico, utilizando a técnica de snowball (bola de neve). Os dados foram coletados entre outubro e dezembro de 2020, por meio de um questionário de caracterização sociodemográfica, familiar e de saúde e uma questão aberta norteadora do estudo. Os depoimentos foram gravados, transcritos e analisados. Resultados: as pessoas idosas relataram vivenciar os sentimentos de preocupação, medo, naturalidade, conforto, com maior predomínio dos sentimentos de desconforto, tristeza, solidão e segurança. Considerações finais: sentimentos positivos e negativos foram vivenciados pelas pessoas idosas durante o período de distanciamento social pela pandemia da Covid-19.

Objetivo: conocer los sentimientos vividos por personas ancianas ante el distanciamiento social en la pandemia de Covid-19. Método: estudio cualitativo que adoptó como referencial la Teoría de las Representaciones Sociales por el método del Discurso del Sujeto Colectivo. Participaron del estudio 29 personas mayores y la selección fue del tipo intencional o teórico, utilizando la técnica de snowball (bola de nieve). Los datos fueron recogidos entre octubre y diciembre de 2020, por medio de un cuestionario de caracterización sociodemográfica, familiar y de salud y una cuestión abierta orientadora del estudio. Las declaraciones fueron grabadas, transcritas y analizadas. Resultados: las personas mayores relataron vivenciar los sentimientos de preocupación, miedo, naturalidad, confort, con mayor predominio de los sentimientos de incomodidad, tristeza, soledad y seguridad. Consideraciones finales: sentimientos positivos y negativos fueron vividos por las personas mayores durante el período de distanciamiento social por la pandemia de Covid-19.

Objective: to know the feelings experienced by elderly people in the face of social distancing in the Covid-19 pandemic. Method: qualitative study that adopted as reference the Theory of Social Representations by the method of Collective Subject Discourse. 29 elderly people participated in the study and the selection was intentional or theoretical, using the snowball technique. Data were collected between October and December 2020, through a questionnaire of sociodemographic, family and health characterization and an open question guiding the study. The statements were recorded, transcribed and analyzed. Results: the elderly reported experiencing feelings of worry, fear, naturalness, comfort, with a greater predominance of feelings of discomfort, sadness, loneliness and security. Final considerations: positive and negative feelings were experienced by the elderly during the period of social distancing due to the Covid-19 pandemic.

Sentiment Analysis in Understanding the Potential of Online News in the Public Health Crisis Response.

This study analyzes online news disseminated throughout the pre-, during-, and post-intervention periods of the "Syphilis No!" Project, which was developed in Brazil between November 2018 and March 2019. We investigated the influence of sentiment aspects of news to explore their possible relationships with syphilis testing data in response to the syphilis epidemic in Brazil. A dictionary-based technique (VADER) was chosen to perform sentiment analysis considering the Brazilian Portuguese language. Finally, the data collected were used in statistical tests to obtain other indicators, such as correlation and distribution analysis. Of the 627 news items, 198 (31.58%) were classified as a sentiment of security (TP2; stands for the news type 2), whereas 429 (68.42%) were classified as sentiments that instilled vulnerability (TP3; stands for the news type 3). The correlation between the number of syphilis tests and the number of news types TP2 and TP3 was verified from (i) 2015 to 2017 and (ii) 2018 to 2019. For the TP2 type news, in all periods, the p-values were greater than 0.05, thus generating inconclusive results. From 2015 to 2017, there was an ρ = 0.33 correlation between TP3 news and testing data (p-value = 0.04); the years 2018 and 2019 presented a ρ = 0.67 correlation between TP3 news and the number of syphilis tests performed per month, with p-value = 0.0003. In addition, Granger's test was performed between TP3 news and syphilis testing, which resulted in a p-value = 0.002, thus indicating the existence of Granger causality between these time series. By applying natural language processing to sentiment and informational content analysis of public health campaigns, it was found that the most substantial increase in testing was strongly related to attitude-inducing content (TP3).

Análisis de sentimiento de tweets sobre la vacuna contra el COVID-19 en países iberoamericanos hispanohablantes / Sentiment analysis of tweets about the COVID-19 vaccine in Spanish-speaking Ibero-American countries

Resumen Introducción: En este estudio se evalúa la emocionalidad asociada a la vacunación contra el COVID-19 a partir de la técnica de análisis de sentimientos de los tweets en países iberoamericanos hispanohablantes. Método: En enero de 2021 se realizó un estudio mixto observacional transversal de 41023 tweets procedentes de nueve países iberoamericanos hispanohablantes (Chile, El Salvador, Venezuela, Ecuador, Argentina, México, Panamá, Perú y España) con una fase cuantitativa y técnicas de análisis de sentimientos mediante algoritmos de inteligencia artificial y una fase cualitativa donde se realizó un análisis del discurso de los tweets cuya emocionalidad era en extremo positiva y negativa. Resultados: A partir del análisis de sentimiento de los tweets, se observó que los países presentan una emocionalidad negativa asociada a la vacunación contra el COVID-19, que se podría atribuir a la desconfianza hacia las autoridades y a la eficacia o seguridad de las vacunas, según el análisis del discurso en los tweets de emocionalidad en extremo negativa. Conclusiones: Las técnicas de análisis de sentimientos en combinación con el análisis del discurso de la emocionalidad extrema posibilitaron la monitorización de las opiniones negativas y sus posibles factores asociados en la vacunación contra el COVID-19 en los países iberoamericanos estudiados.

Abstract Introduction: This study evaluates the emotionality associated with vaccination against COVID-19 using the sentiment analysis technique of tweets in Spanish-speaking Ibero-American countries. Method: In January 2021 a mixed cross-sectional observational study of 41023 tweets from nine Spanish-speaking Ibero-American countries (Chile, El Salvador, Venezuela, Ecuador, Argentina, Mexico, Panama, Peru and Spain) was carried out with a quantitative phase and analysis techniques of feelings based on artificial intelligence algorithms and a qualitative phase where an analysis of the discourse of the tweets whose emotionality was extremely positive and negative was carried out. Results: From the sentiment analysis of the tweets, it was observed that the countries present a negative emotionality associated with the vaccination against COVID-19, which could be attributed to mistrust towards the authorities and the efficacy or safety of the vaccines, according to the analysis of the discourse in the extremely negative emotionality tweets. Conclusions: Sentiment analysis techniques in combination with extreme emotionality discourse analysis made it possible to monitor negative opinions and their possible associated factors in vaccination against COVID-19 in the Ibero-American countries studied.

Do sentiments of professors feedback change after migrating from in-person to online modalities? Pre- and during COVID-19 experience.

The COVID-19 pandemic forced higher education institutions to alter how they offer classes at an unprecedented pace. Due to ambiguities and lockdown restrictions, the transition phase negatively impacted students' and professors emotions. As a result, lecturers had to cope with unfamiliar online class teaching responsibilities and develop new teaching dynamics. This work aims to analyze one of the most adversely affected procedures of teaching, the written feedback provided to students. This research strives to explore whether the professors' feedback style altered from face-to-face education to online education on digital platforms during the COVID-19 restrictions. This exploratory-design study uses a mixed methodology to explain the subject on hand based on data collected from 117 undergraduate students. Sentiment lexicographers are utilized to address and identify the emotions expressed in the texts. Trust was the most frequent emotion expressed in face-to-face and online courses. It is also observed that the sentiments of joy and sadness changed significantly among online and face-to-face groups based on the professors' feedback style and approach. Finally, the study reveals that the joy words and the sadness words associated with the learning process are the most commonly utilized sentiments. This study suggests that when the courses transitioned from face-to-face to online learning, the professors' feedback changed to a more positive feeling that expressed appreciation for the students' work, encouraging them to strive for their complete academic development, and usher them into a better learning environment.

Analyzing Spanish-Language Public Sentiment in the Context of a Pandemic and Social Unrest: The Panama Case.

Over the past decade, an increase in global connectivity and social media users has changed the way in which opinions and sentiments are shared. Platforms such as Twitter can act as public forums for expressing opinions on non-personal matters, but often also as an outlet for individuals to share their feelings and personal thoughts. This becomes especially evident during times of crisis, such as a massive civil disorder or a pandemic. This study proposes the estimation and analysis of sentiments expressed by Twitter users of the Republic of Panama during the years 2019 and 2020. The proposed workflow is comprised of the extraction, quantification, processing and analysis of Spanish-language Twitter data based on Sentiment Analysis. This case of study highlights the importance of developing natural language processing resources explicitly devised for supporting opinion mining applications in Latin American countries, where language regionalisms can drastically change the lexicon on each country. A comparative analysis performed between popular machine learning algorithms demonstrated that a version of a distributed gradient boosting algorithm could infer sentiment polarity contained in Spanish text in an accurate and time-effective manner. This algorithm is the tool used to analyze over 20 million tweets produced between the years of 2019 and 2020 by residents of the Republic of Panama, accurately displaying strong sentiment responses to events occurred in the country over the two years that the analysis performed spanned. The obtained results highlight the potential that methodologies such as the one proposed in this study could have for transparent government monitoring of responses to public policies on a population scale.

Detecting Depression Signs on Social Media: A Systematic Literature Review.

Among mental health diseases, depression is one of the most severe, as it often leads to suicide; due to this, it is important to identify and summarize existing evidence concerning depression sign detection research on social media using the data provided by users. This review examines aspects of primary studies exploring depression detection from social media submissions (from 2016 to mid-2021). The search for primary studies was conducted in five digital libraries: ACM Digital Library, IEEE Xplore Digital Library, SpringerLink, Science Direct, and PubMed, as well as on the search engine Google Scholar to broaden the results. Extracting and synthesizing the data from each paper was the main activity of this work. Thirty-four primary studies were analyzed and evaluated. Twitter was the most studied social media for depression sign detection. Word embedding was the most prominent linguistic feature extraction method. Support vector machine (SVM) was the most used machine-learning algorithm. Similarly, the most popular computing tool was from Python libraries. Finally, cross-validation (CV) was the most common statistical analysis method used to evaluate the results obtained. Using social media along with computing tools and classification methods contributes to current efforts in public healthcare to detect signs of depression from sources close to patients.

Sociocultural factors during COVID-19 pandemic: Information consumption on Twitter.

The purpose of the research is to describe the sociocultural factors that emerged during the COVID-19 pandemic. Twitter is used as an instrument for data collection. The study is qualitative and uses the netnographic method. To analyze the flow of messages posted on Twitter, the model proposed by Perez-Cepeda and Arias-Bolzmann (2020), which describes sociocultural factors, is taken as a basis. The semantics that people use are a type of functional knowledge that reveals sociocultural factors. Sentiments were analyzed through lexicon-based methods, which are the most suitable. The categorization and classification of the data are performed based on the information that users post on Twitter. The tweets related to COVID-19 describe the sociocultural issues and the level of sentiment around the pandemic. The discussion centers on the COVID-19 pandemic, information consumption, lexicon, sociocultural factors and sentiment analysis. The study was limited to the social media Twitter; another limitation was not to consider the social group of the users who interact with @pandemic_Covid-19, official account of the World Health Organization (WHO). This research contributes to the social sciences, focusing on sociocultural interaction through the use of the social network Twitter. It describes the link between sociocultural factors and the level of sentiment on issues related to the COVID-19 pandemic.