Positive Sentiment Research Articles

Academic Coverage of Online Activism of Disabled People: A Scoping Review

Disabled people need to be activists given the many problematic lived realities they face. However, they frequently encounter obstacles in traditional offline activism. Online activism could be a potential alternative. The objective of this scoping review is to examine the extent and nature of the coverage of disabled people in the academic literature that focuses on online activism. We searched the abstracts in Scopus, Web of Science, and the 70 databases in EBSCO-HOST for the presence of 57 terms linked to online activism or online tools or places for online activism, which generated 18,069 abstracts for qualitative analysis. Of the 18,069 abstracts, only 54 discussed online the activism by disabled people. Among these 54 relevant abstracts, only one contained the term “Global South”. No relevant abstracts were found that contained the terms “Metaverse” or “Democrac*” together with “activis*”. Only two relevant abstracts contained the phrase “digital citizen*”. Out of the 57 terms, 28 had no hits. The thematic analysis identified 24 themes: 6 themes in 30 abstracts had a positive sentiment, 7 themes in 30 abstracts had a negative sentiment, and 11 themes present in 23 abstracts had a neutral sentiment. There were three main themes: the positive role and use of online activism; the technical accessibility barriers to online activism; and the attitudinal accessibility problems arising from ableist judgments. The intersectionality of the disability identity with other marginalized identities and the issue of empowerment were rarely addressed, and ability judgment-based concepts beyond the term’s “ableism” and “ableist” were not used. The study underscores the necessity for further research given the few relevant abstracts found. The study also indicates that actions are needed on barriers to online activism and that examples for best practices exist that could be applied more often. Future studies should also incorporate a broader range of ability judgment-based concepts to enrich the analysis and to support the empowerment of disabled activists.

Tree hierarchical deep convolutional neural network optimized with sheep flock optimization algorithm for sentiment classification of Twitter data

ABSTRACT The increasing volume of online reviews and tweets poses significant challenges for sentiment classification because of the difficulty in obtaining annotated training data. This paper aims to enhance sentiment classification of Twitter data by developing a robust model that improves classification accuracy and computational efficiency. The proposed method named Tree Hierarchical Deep Convolutional Neural Network optimized with Sheep Flock Optimization Algorithm for Sentiment Classification of Twitter Data (SCTD-THDCNN-SFOA) utilizes the Stanford Sentiment Treebank dataset. The process begins with pre-processing steps including Tokenization, Stop words Elimination, Filtering, Hashtag Removal, and Multiword Grouping. The Gray Level Co-occurrence Matrix Window Adaptive Algorithm is employed to extract features, such as emoticon counts, punctuation counts, gazetteer word existence, n-grams, and part of speech tags. These features are selected using Entropy–Kurtosis-based Feature Selection approach. Finally, the Tree Hierarchical Deep Convolutional Neural Network enhanced by the Sheep Flock Optimization Algorithm is used to categorize the Twitter data as positive, negative, and neutral sentiments. The proposed SCTD-THDCNN-SFOA method demonstrates superior performance, achieving higher accuracy and lesser computation time than the existing models, respectively. The SCTD-THDCNN-SFOA framework significantly improves the accuracy and efficiency of sentiment classification for Twitter data.

Nested Sentiment Analysis for ESG Impact: Leveraging FinBERT to Predict Market Dynamics Based on Eco-Friendly and Non-Eco-Friendly Product Perceptions with Explainable AI

In the current era, the environmental component of ESG is recognized as a major driver due to the pressing challenges posed by climate change, population growth, global warming, and shifting weather patterns. The environment must be considered a critical factor, and as evidenced by existing research, it is regarded as the dominant component within ESG. In this study, the ESG score is derived primarily from the environmental score. The increasing importance of the environmental, social, and governance (ESG) factors in financial markets, along with the growing need for sentiment analysis in sustainability, has necessitated the development of advanced sentiment analysis techniques. A predictive model has been introduced utilizing a nested sentiment analysis framework, which classifies sentiments towards eco-friendly and non-eco-friendly products, as well as positive and negative sentiments, using FinBERT. The model has been optimized with the AdamW optimizer, L2 regularization, and dropout to assess how sentiments related to these product types influence ESG metrics. The “black-box” nature of the model has been addressed through the application of explainable AI (XAI) to enhance its interpretability. The model demonstrated an accuracy of 91.76% in predicting ESG scores and 99% in sentiment classification. The integration of XAI improves the transparency of the model’s predictions, making it a valuable tool for decision-making in making sustainable investments. This research is aligned with the United Nations’ Sustainable Development Goals (SDG 12 and SDG 13), contributing to the promotion of sustainable practices and fostering improved market dynamics.

Do Financial Community’s Tweet Sentiments Affect the Stock Market? Evidence from an Emerging Economy

In the era of social media and internet, Twitter (presently ‘X’) has been recognized as the prominent factor for financial markets by multiple corporates and experts. The motivation of present research hinges on burgeoning popularity of Twitter in the financial community for sharing information and impact of their hidden sentiments on financial markets. Research on tweet-based sentiments and their influence on financial markets in India is still in the infancy stage. To cover this gap, the present study empirically investigates the predictive impact of financial tweet sentiments on the Indian stock Index’s returns over a short period. The paper scraped 22,816 tweets and Nifty 50 index’s intraday hour end values for January 2023. Using Natural Processing Technique, the overall sentiments of the financial community are found as highly positive for each trading hour. Next, it presents a low positive correlation between positive sentiments, sentiment polarity, and hourly returns. Lagged negative sentiments reflect the better ability to impact current period returns than positive sentiments and polarity. Additionally, sentiment polarity and returns reflect bi-directional causation for the future period impact. Investors, stock market traders, and other practitioners can employ the proven concept of linkage between tweet sentiments and returns for constructing effective investment and trading strategies. JEL Classifications: G41, D53, G17, G02, G19

Exploring information demand in the maritime industry: a Google Trends approach

ABSTRACT The fundamental impetus for this research is the documented wide use of Google and the ensuing applications of Google Trends in behavioural economics. Expanding the existing literature, we focus on addressing the question of whether information demand from individual investors can reflect a freight rates sentiment. This is achieved by analysing Google queries referring to investment in maritime stocks and other related markets. These searches comprise essentially a blend of information and intuition. More specifically, we examine whether the most frequently searched term and topic referred to Baltic Dry Index (BDI) can serve as a reliable and valid positive or negative sentiment market proxies. We find a negative impact on freight rates, capital markets, and the number of available dry bulk fleet, providing evidence aligned with the risk-averse investor theory. We unveil bidirectional interactions between our sentiment index and the two out of three examined maritime markets, supporting the rejection of the efficient market hypothesis. Our study also verifies the similar effects of information demand and media-produced sentiment indexes on the corresponding market. We succeed in confirming sentiment’s predictability out-of-sample for BDI. Beyond the academic value, our findings suggest a manner of enhancing the accuracy of freight rates predictions for practitioners.

Too much of a good thing: Addressing the shape of relationship between positive media sentiment and IPO performance

The influence of mass media sentiment on the IPO performance of newly listed firms has received increasing research attention in management and entrepreneurship research. However, prior literature assumes the beneficial role of positive media sentiment in investors’ evaluation of the firm yet overlooks the potential downside of too much positive media sentiment. Based on two theories, dual processing theory and celebrity theory, we develop and test a curvilinear relationship between positive media sentiment and newly listed firms’ IPO performance. Further, we suggest that IPO market conditions, offering an important information cue, moderate this curvilinear relationship. Drawing a representative sample of newly listed U.S. firms in recent decades, we find support for the proposed inverted U-shape relationship and a moderating effect of market conditions.

Automatic detection of fake reviews at marketplaces using expert-based features and consumers’ reactions

The article presents the results of a practical study of the features of fake reviews that are described by marketers and other experts. Due to the abundance of fake reviews on marketplaces, consumer trust falls not only in the seller or platform, but in the genre itself. The paper presents the results of automatic classification of reviews from Russian marketplaces into potentially fake and honest ones using modelling of features that experts call labels of a fake review (presence of template words, exclamation marks, emoji, positive sentiment), and machine learning algorithms. To solve the problem, a corpus of 6 288 texts from the Russian marketplaces Wildberries and Megamarket has been collected. The target variable (predicted class) is the ratio of likes and dislikes given to the review by other buyers. The best result is demonstrated by the support vector machine algorithm in binary classification into reviews with a low and high ratings (without neutral ones). The classification model confirms that the formal features identified by experts as indicating fake reviews indeed have predictive potential. The quality of the model is reduced by the imbalance in classes and insufficient number of reviews with buyer reactions in our corpus, which leaves room for further work.

Challenges and Solutions in Scaling Operations Globally: A Guide for U.S. Food and BeverageExecutives

Abstract—This paper explores the challenges and solutions associated with scaling operations globally in the food and beverage sector from a U.S. executive’s perspective. It examines why expanding internationally makes sense for companies with successful domestic products, highlighting the advantages of U.S. innovation and lower costs of innovation compared to local markets abroad. The paper outlines a strategic roadmap for global expansion, including test markets, partnerships with local firms, and focusing on countries with positive sentiments toward U.S. food safety standards. A hypothetical case study illustrates the growth process, emphasizing the need for internal buy-in, patience, and a long-term view. Potential pitfalls and strategies for success are discussed, along with competitive analysis and the role of disruption in scaling products. Index Terms—Global Expansion, Food and Beverage Industry, Consumer Packaged Goods (CPG), International Operations, U.S. Innovation, Market Entry Strategy, Competitive Analysis, Disruption

Sentiment Analysis of User Reviews on BSI Mobile and Action Mobile Applications on the Google Play Store Using Multinomial Naive Bayes Algorithm

Mobile banking services are designed to facilitate customer transactions. Bank Syariah Indonesia (BSI) and Bank Aceh also provide these online services through their respective applications, BSI Mobile and Action Mobile. The mobile banking apps aim to simplify customer transactions, which can be conducted remotely via several features, from transfers, payments, and purchases to zakat payments, by simply connecting to the internet. Therefore, this research aims to classify the sentiment of user reviews for BSI Mobile and Action Mobile applications on Google Play Store to understand the users' experiences. The Multinomial Naïve Bayes algorithm is used in this study, where the algorithm analyzes and classifies the user reviews into positive and negative sentiment categories. The study involves several stages, such as text preprocessing, sentiment visualization, splitting the data into an 80:20 ratio for training and testing datasets, and training the model using the Multinomial Naïve Bayes algorithm. The results of this study show that the Multinomial Naïve Bayes algorithm performs well in analyzing user sentiment for BSI Mobile and Action Mobile, achieving an accuracy of 78.7%, precision of 76.5%, recall of 86.2%, and an F1-score of 80.6% for BSI Mobile, and an accuracy of 85.6%, precision of 75%, recall of 75%, and an F1-score of 75% for Action Mobile. Additionally, the sentiment classification results reveal that 52.8% of BSI Mobile user reviews are positive and 47.2% are negative, while for Action Mobile, 35.1% are positive and 64.9% are negative. For BSI Mobile, 21,497 reviews express a positive sentiment with dominant keywords such as "updated," "good," "balance," "transaction," and "thank." Meanwhile, for Action Mobile, 274 reviews express a negative sentiment with dominant keywords such as "transaction," "application," "network," "register," "please," and "update."

Treatment Recommendation using BERT Personalization

This research work develops a new framework that combines patient feedback with evidence-based best practices across disease states to improve drug recommendations. It employs BERT as its free-text processing engine to deal with sentiment judgment and classification. The functionality of the system, named `PharmaBERT`, includes acceptance of drug review data as a comprehensive input, drug categorization when dealing with a wide range of treatments and fine-tuning the BERT-based model for gaining positive or negative sentiment towards specific medications. PharmaBERT categorizes various drugs and fine-tunes the BERT structure to perceive lots of possible sentiments for specific medications. Consequently, PharmaBERT brings all its training and optimization capabilities together and through this, the system reaches a higher accuracy of up to 91% thus showcasing the potency of the model in capturing patient sentiments. While being a BERT spin-off, PharmaBERT utilizes its own set of experienced techniques to comprehend and sense the health-related text input given by the patient, doctor, or pharmacist. It uses transfer learning, that is, it learns from language representations to adapt quickly to the intricacies of drug reviewing. Through PharmaBERT, healthcare professionals may expand their diagnoses with insights from patient feedback to constitute more neutral decisions.

Analysis Of Opinion Sentiment Towards Electric Vehicle Tax On Social Media X Using The Support Vector Machine (SVM) Method

Electric vehicle tax is increasingly becoming an important issue related to environmental and fiscal policies. Electric vehicles are considered an environmentally friendly solution to reduce greenhouse gas emissions and dependence on fossil fuels. However, public perception of electric vehicle tax is still mixed. This study aims to analyze public sentiment about electric vehicle tax based on data from social media platform X, using the Support Vector Machine (SVM) method. The data used was taken through a crawling technique with a total of 1,014 valid data. The data was then classified into positive and negative classes with a transformer. In this analysis, the data was divided with a ratio of 8:2 between training data and test data. 811 were used as training data and 203 as test data. The research stages involved data preprocessing, sentiment labeling, data separation into training and test data, and weighting using TF-IDF. After that, SVM was applied to classify tweets into positive and negative sentiments. The test results showed that the SVM algorithm had an accuracy of 79%, precision of 85%, recall of 89%, and F1-score of 87%. Based on the results of this study, some people feel unsure about the government's policy regarding electric vehicle tax, because it is considered unfair to the lower middle class. Electric vehicles are considered more expensive than fuel-powered vehicles, so this policy is considered unprofitable.

Revisiting inter‐Korean events and South Koreans’ perception on unification

AbstractObjectiveThis study aims to examine how inter‐Korean events influence South Korean public opinion on unification. It seeks to understand the impact of various political events, including South‐North contacts and North Korean nuclear provocations, on South Koreans' attitudes towards unification.MethodsUsing annual survey data from the Institute for Peace and Unification Studies at Seoul National University (IPUS), this study creates a novel time‐series dataset spanning from 2007 to 2021. We test how each of the inter‐Korean political events affects support for unification and how key individual dispositions moderate the level of support.ResultsThe empirical findings reveal that inter‐Korean summits and family reunions positively affect unification sentiment, with family reunions showing surprisingly stronger impacts than inter‐Korean summits. Regarding North Korea's provocations, nuclear tests negatively influence public support for unification, while ballistic missile launches do not significantly impact unification sentiment, likely due to public habituation.ConclusionThis study contributes to existing literature by providing new empirical insights into the relationship between inter‐Korean political events and public opinion. The findings suggest that policymakers should focus on strategic short‐term initiatives, particularly increasing inter‐Korean contacts, to foster long‐term positive sentiment towards unification. However, it's important to note that individual variables become more influential in shaping public opinion over the long term.

"Ups and Downs, Joys and Sorrows" - Assessment and Clinical Relevance of Patient Priorities in an Interdisciplinary Parkinson's Disease Clinic.

Barriers to communication and inaccurate provider assumptions about patient priorities limit the delivery of comprehensive, high-quality, patient-centered care (PCC) to people with Parkinson's (PWP). This study aimed to analyze priorities of PWP using a qualitative, unstructured single-question survey and to test associations with validated quality of life (QOL) measures. During appointments at a subspecialty, interdisciplinary clinic, PWP (n=139) provided written responses to the prompt: "What is important for your care team to know about you?" Patient Health Questionnaire, Montreal Cognitive Assessment, and Hoehn and Yahr scales were obtained through retrospective chart review. Key qualitative themes were identified through grounded theory analysis, and associations with quantitative health measures were tested with correlation analyses. Common themes included participant health (eg, PD-related goals and comorbidities), non-illness identities (eg, family or community role), and the psychosocial impact of PD (eg, losing independence and uncertainty). Positive sentiments (n=73), such as motivation and optimism, were more common than negative sentiments (n=45), such as loss and fear. There was moderate concordance between worsened mental health and uncertainty (rho=0.206, p=0.02) and inverse concordance between worsened mobility and gratitude (rho=-0.174, p=0.04). The range of priorities that PWP want to share with their care team is more diverse than that of common provider assumptions, is correlated with clinical outcomes such as mental health and mobility, and may not be captured by existing QOL assessment tools. An open-ended, qualitative prompt should be incorporated into routine specialist care for PWP as a valuable QOL indicator.

Analisis Sentimen Opini Publik Terhadap Cyberbullying Pada Komentar Instagram Menggunakan Multinomial Naive Bayes

This study aims to evaluate the effectiveness of a sentiment classification model using the Naïve Bayes algorithm on Instagram comment data. The main focus of this research is to measure the performance of the model in terms of accuracy, precision, recall, and F1-score. The data used in this study includes 400 Instagram comments that have been labeled with negative and positive sentiments. Data pre-processing involved case folding, tokenization, stopword removal, and stemming, followed by TF-IDF weighting to measure the importance of each word. The data was divided into 80% for training and 20% for testing. The Naïve Bayes model was then applied to the test data to predict sentiment. The evaluation results show that the model achieved an accuracy of 86.25%, with a precision of 85.56%, recall of 86.46%, and F1-score of 85.88%. For the negative class, the precision reached 91%, recall 85%, and F1-score 88%, while for the positive class, the precision was 80%, recall 88%, and F1-score 84%. These findings show that the Naïve Bayes model is effective in classifying the sentiment of Instagram comments and provides useful insights in understanding public sentiment towards the issue of cyberbullying

Use of AI in Mental Health Care: Community and Mental Health Professionals Survey.

Artificial intelligence (AI) has been increasingly recognized as a potential solution to address mental health service challenges by automating tasks and providing new forms of support. This study is the first in a series which aims to estimate the current rates of AI technology use as well as perceived benefits, harms, and risks experienced by community members (CMs) and mental health professionals (MHPs). This study involved 2 web-based surveys conducted in Australia. The surveys collected data on demographics, technology comfort, attitudes toward AI, specific AI use cases, and experiences of benefits and harms from AI use. Descriptive statistics were calculated, and thematic analysis of open-ended responses were conducted. The final sample consisted of 107 CMs and 86 MHPs. General attitudes toward AI varied, with CMs reporting neutral and MHPs reporting more positive attitudes. Regarding AI usage, 28% (30/108) of CMs used AI, primarily for quick support (18/30, 60%) and as a personal therapist (14/30, 47%). Among MHPs, 43% (37/86) used AI; mostly for research (24/37, 65%) and report writing (20/37, 54%). While the majority found AI to be generally beneficial (23/30, 77% of CMs and 34/37, 92% of MHPs), specific harms and concerns were experienced by 47% (14/30) of CMs and 51% (19/37) of MHPs. There was an equal mix of positive and negative sentiment toward the future of AI in mental health care in open feedback. Commercial AI tools are increasingly being used by CMs and MHPs. Respondents believe AI will offer future advantages for mental health care in terms of accessibility, cost reduction, personalization, and work efficiency. However, they were equally concerned about reducing human connection, ethics, privacy and regulation, medical errors, potential for misuse, and data security. Despite the immense potential, integration into mental health systems must be approached with caution, addressing legal and ethical concerns while developing safeguards to mitigate potential harms. Future surveys are planned to track use and acceptability of AI and associated issues over time.

Sentiment Analysis of Dune: Part Two Movie Reviews Using the Naive Bayes Method

Research on films is fascinating because of the profound changes that the development of information and communication technology has brought about in our interactions with and consumption of media content. This study performs sentiment analysis on "Dune: Part Two" movie reviews using the Naïve Bayes method. Review data was collected from IMDb and then processed through several stages such as preprocessing, feature selection with TF-IDF, data splitting, and data mining and evaluation. Naïve Bayes was chosen for its simplicity and ability to handle large datasets effectively. The test results showed a high accuracy rate of 95%, indicating that this model can identify positive, negative, and neutral sentiments well. The use of TF-IDF in feature selection allowed the model to focus on important words, enhancing its sentiment classification ability. This research can provide insights into audience perceptions of the film "Dune: Part Two," which is beneficial for the film industry.

Sentiment analysis of post-COVID-19 health information needs of autism spectrum disorder community: insights from social media discussions.

This study explores the health information needs of individuals with autism spectrum disorder (ASD) and their caregivers in the post-COVID-19 era by analyzing discussions from Reddit, a popular social media platform. Utilizing a mixed-method approach that integrates qualitative content analysis with quantitative sentiment analysis, we analyzed user-generated content from the "r/autism" subreddit to identify recurring themes and sentiments. The qualitative analysis uncovered key themes, including symptoms, diagnostic challenges, caregiver experiences, treatment options, and stigma, reflecting the diverse concerns within the ASD community. The quantitative sentiment analysis revealed a predominance of positive sentiment across discussions, although significant instances of neutral and negative sentiments were also present, indicating varied experiences and perspectives among community members. Among the machine learning models used for sentiment classification, the Bi-directional Long Short-Term Memory (Bi-LSTM) model achieved the highest performance, demonstrating a validation accuracy of 95.74%. The findings highlight the need for improved digital platforms and community resources to address the specific health information needs of the ASD community, particularly in enhancing access to reliable information and fostering supportive environments. These insights can guide future interventions and policies aimed at improving the well-being of autistic persons and their caregivers.

ANALISIS SENTIMEN APLIKASI MICROSOFT TEAMS BERDASARKAN ULASAN GOOGLE PLAY STORE MENGGUNAKAN MODEL NEURAL NETWORK DENGAN OPTIMASI ADAPTIVE MOMENT ESTIMATION (ADAM)

The Covid-19 virus pandemic with its rapid spread has made the government enact several policies that can reduce community activities that have the potential to cause crowds including social distancing, work from home (WFH) and online and hybrid learning. One of the platforms that was widely used during the WFH and online learning period was Microsoft Teams. Microsoft Teams is an app that lets people and organizations collaborate and interact online. Microsoft Teams can be downloaded through the Google Play Store and users can leave reviews. Sentiment analysis is used to classify user sentiment towards Microsoft Teams into positive and negative sentiments. Review data obtained from January to November 2022. The classification is carried out using the Neural Network which is a computational system inspired by biological neural networks. Weight optimization is done using Adaptive Moment Estimation which is popular in the field of deep learning because it achieves good results quickly. The classification model in this paper was built with a training test ratio of 80:20. The best hyperparameter combination is node hidden layer 100, learning rate 0.0001, and batch size 32. The accuracy of the ANN classification model was obtained by 88.16%.Keywords: Microsoft Teams, Google Play Store, Sentiment Analysis, Neural Network, Adaptive Moment Estimation

“Between the Lines”: Perceptions of Poetry With Authorship Attributed to Artificial Intelligence or Humans – A Comparative Analysis

ABSTRACTIn the rapidly evolving field of artificial intelligence (AI) literature generation, understanding how society perceives AI‐generated content, compared with human‐produced literature is of paramount importance. This study investigated societal perceptions and biases toward AI‐generated versus human‐produced poetry. A sample of 123 participants was subjected to a controlled experiment in which they evaluated a human‐generated poem that was randomly attributed to either a human, an AI, or an unspecified author. The assessment metrics comprised five categories: originality, aesthetic appeal, emotional engagement, coherence, and interpretive difficulty. An analysis of variance was used to analyze the survey results. Our findings revealed that poems attributed to an AI consistently received lower scores for originality, aesthetic appeal, and emotional engagement compared to those attributed to a human author. However, AI‐generated content was perceived as more complex and was rated higher in terms of interpretive difficulty. Interestingly, perceived authorship did not significantly influence coherence as a metric. When the poem was believed to be AI‐generated, it faced more critical evaluations than when it was human‐attributed. When authorship was ambiguous, feedback was distributed uniformly across negative, positive, and neutral sentiments, suggesting a potential mitigating effect of ambiguity on bias.

Evaluation of patient satisfaction with an enhanced recovery protocol for esophageal resections: a concurrent quantitative and qualitative analysis.

Patient satisfaction during hospitalization for esophagectomy has been little studied. The aim of this study was to evaluate patients' satisfaction with a newly introduced enhanced recovery protocol (ERP) for esophagectomy. At hospital discharge, patients were invited to complete a questionnaire. This pseudonymized questionnaire contained 5-point Likert scales regarding items on multidisciplinary care (n = 7), information/communication (n = 7), length of stay (n = 1), and specific adaptations of care in the ERP (n = 11). One open question asked for patient experiences and suggestions for improving the ERP. Between May 2017 and December 2021, 521 patients were included in the ERP after esophagectomy. Of them, 327 patients (63%) completed the questionnaire. Response rates were evenly distributed between genders and slightly higher in younger patients (<60years; 68%) as compared to elderly patients (>70years; 60%). Quantitative analysis revealed high satisfaction rates for multidisciplinary care (86.8%), information/communication (84.9%), and ERP adaptations (82.2%), and length of stay was considered optimal in 80%. There were no significant differences in satisfaction observed between gender nor age groups. For the qualitative analysis, there were 108 open answers, resulting in 268 statements. Sentiments expressed in these statements were evaluated as negative, positive, or unspecified. Negative sentiments were attributable to alimentation, organizational factors, and communication. Positive sentiments were attributed to interpersonal relations, multidisciplinary care, and ERP. Overall, patients are very satisfied with the ERP for esophagectomy during hospitalization. By incorporating qualitative data, the results of this quantitative analysis are expanded and elucidated, showing areas where improvements to our ERP are possible to increase patient satisfaction.