Using Text Mining and Sentiment Analysis To Explore Tourists Consumer Focus From Online Reviews – Taking Mausoleum Of The First Qin Emperor As Example

With the development of the economy, high-quality free travel has become a mainstream leisure tourism method, and tourism-related information has also grown exponentially. Coupled with the diversity of information sources, tourist attraction consumers received a lot of fragmented information. Previous research pointed out that tourist attraction consumers\u27 decision-making basis is increasingly relying on electronic word of mouth. However, the variety of reviews on the Internet makes it easier for tourist attraction consumers to make timely or even wrong judgments due to information integration errors. In order to solve the problems mentioned above, this research is based on big data text mining and sentiment analysis processing analysis, using the existing electronic travel review data to conduct mining analysis, in order to recommend the most useful review information to tourist attraction consumers, allowing tourist attraction consumers to make effective decisions. In other words, tourist attraction consumers can enable users to get advance reminders before making decision and presented with visualization. In this way, tourists who are consumers of tourist attractions can receive the information they need quickly and logically, and quickly make decision. Then, improve user satisfaction. Finally, results provide tourist attractions operators as a reference to improve and strengthen their core business contents and priorities

Semantic-Level New Information Identification in Electronic Health Records Using Text-Mining Techniques

Electronic health records (EHRs) are widely used in healthcare systems to store and transmit patients’ health records. They have many advantages, such as saving space, increasing efficiency, and facilitating communication. However, they also have a major drawback: information redundancy. Healthcare professionals often use copy and paste to write clinical notes, which leads to excessive similarity and low diversity in EHRs. This impairs the readability and quality of EHRs and hinders decision making. To address this problem, this study proposes a text-mining approach to identify new information at semantic-level in EHRs. Unlike previous studies that focused on word-level identification, we use concept occurrence and concept similarity score methods to annotate new information at semantic-level and evaluate them with gold standards. The experimental evaluation demonstrates that the method proposed in this study achieves an F1-score ranging from 78.57 to 80.31 under various parameter combinations. The proposed method enables healthcare professionals to read EHRs more efficiently and make more informed decisions

Emotional Reactions in Information Dissemination Through the Lens of SOR Theory

Social media has changed the readers’ consumption of news. Traditionally, news reports must be neutral and objective. However, digital news is inundated with emotion-laden clickbait, which increases the likelihood of users being exposed to a negative news environment. As such, this study explores the chain effect of information sentiment dissemination in relation to the generation characteristics of news text content. News is collected from mainstream media and a BERT model is trained to classify the headlines that influence readers and listeners’ emotions. In addition, stimulus-organism-response theory is used to understand the stimuli effects of online news in social media on audiences, including if readers are willing to turn psychological stimuli into action reposts, subsequent emotional expression or discussion of topics, and other stimuli-response chain reactions. Results show that the sentiment in news headlines significantly affects the readers’ dissemination behavior and subsequent emotional responses. This study allows journalism and their readers to understand the challenges they face in the digital news environment

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1

A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios-such as in monitoring disease progression of coronavirus disease 2019-to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchi labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests for long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.Comment: 48 pages, 8 figures. To be submitte

The Atacama Large Millimeter/submillimeter Array (ALMA) Band-1 Receiver

The Atacama Large Millimeter/submillimeter Array(ALMA) Band 1 receiver covers the 35-50 GHz frequency band. Development of prototype receivers, including the key components and subsystems has been completed and two sets of prototype receivers were fully tested. We will provide an overview of the ALMA Band 1 science goals, and its requirements and design for use on the ALMA. The receiver development status will also be discussed and the infrastructure, integration, evaluation of fully-assembled band 1 receiver system will be covered. Finally, a discussion of the technical and management challenges encountered will be presented

Psoas muscle area is an independent survival prognosticator in patients undergoing surgery for long-bone metastases

Background: Predictive analytics is gaining popularity as an aid to treatment planning for patients with bone metastases, whose expected survival should be considered. Decreased psoas muscle area (PMA), a morphometric indicator of suboptimal nutritional status, has been associated with mortality in various cancers, but never been integrated into current survival prediction algorithms (SPA) for patients with skeletal metastases. This study investigates whether decreased PMA predicts worse survival in patients with extremity metastases and whether incorporating PMA into three modern SPAs (PATHFx, SORG-NG, and SORG-MLA) improves their performance. Methods: One hundred eighty-five patients surgically treated for long-bone metastases between 2014 and 2019 were divided into three PMA tertiles (small, medium, and large) based on their psoas size on CT. Kaplan–Meier, multivariable regression, and Cox proportional hazards analyses were employed to compare survival between tertiles and examine factors associated with mortality. Logistic regression analysis was used to assess whether incorporating adjusted PMA values enhanced the three SPAs' discriminatory abilities. The clinical utility of incorporating PMA into these SPAs was evaluated by decision curve analysis (DCA). Results: Patients with small PMA had worse 90-day and 1-year survival after surgery (log-rank test p 0.5 after the addition of adjusted PMA to these SPAs. Conclusions: Decreased PMA on CT is associated with worse survival in surgically treated patients with extremity metastases, even after controlling for three contemporary SPAs. Physicians should consider the additional prognostic value of PMA on survival in patients undergoing consideration for operative management due to extremity metastases

Use and effectiveness of dapagliflozin in patients with type 2 diabetes mellitus: a multicenter retrospective study in Taiwan

Aims/Introduction To investigate the clinical outcomes of patients with type 2 diabetes mellitus (T2DM) who initiated dapagliflozin in real-world practice in Taiwan. Materials and Methods In this multicenter retrospective study, adult patients with T2DM who initiated dapagliflozin after May 1st 2016 either as add-on or switch therapy were included. Changes in clinical and laboratory parameters were evaluated at 3 and 6 months. Baseline factors associated with dapagliflozin response in glycated hemoglobin (HbA1c) were analyzed by univariate and multivariate logistic regression. Results A total of 1,960 patients were eligible. At 6 months, significant changes were observed: HbA1c by −0.73% (95% confidence interval [CI] −0.80, −0.67), body weight was -1.61 kg (95% CI −1.79, −1.42), and systolic/diastolic blood pressure by −3.6/−1.4 mmHg. Add-on dapagliflozin showed significantly greater HbA1c reduction (−0.82%) than switched therapy (−0.66%) (p = 0.002). The proportion of patients achieving HbA1c <7% target increased from 6% at baseline to 19% at Month 6. Almost 80% of patients experienced at least 1% reduction in HbA1c, and 65% of patients showed both weight loss and reduction in HbA1c. Around 37% of patients had at least 3% weight loss. Multivariate logistic regression analysis indicated patients with higher baseline HbA1c and those who initiated dapagliflozin as add-on therapy were associated with a greater reduction in HbA1c. Conclusions In this real-world study with the highest patient number of Chinese population to date, the use of dapagliflozin was associated with significant improvement in glycemic control, body weight, and blood pressure in patients with T2DM. Initiating dapagliflozin as add-on therapy showed better glycemic control than as switch therapy