Predicting User Satisfaction of Mobile Healthcare Services Using Machine Learning

Lee, Haein; Lee, Seon Hong; Nan, Dongyan; Kim, Jang Hyun

doi:10.4018/joeuc.300766

Cited by 6 publications

(4 citation statements)

References 63 publications

(91 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They found that a convolutional neural network (CNN) performed the best in this task. In [29,30], machine-learning methods were utilized to improve user satisfaction through application reviews. Along with LightGBM, logistic regression and XGBoost yielded prominent outcomes.…”

Section: Machine Learning For Predictionmentioning

confidence: 99%

Predicting Bitcoin Trends Through Machine Learning Using Sentiment Analysis with Technical Indicators

Jung¹,

Lee²,

Lee³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market. As the history of the Bitcoin market is short and price volatility is high, studies have been conducted on the factors affecting changes in Bitcoin prices. Experiments have been conducted to predict Bitcoin prices using Twitter content. However, the amount of data was limited, and prices were predicted for only a short period (less than two years). In this study, data from Reddit and LexisNexis, covering a period of more than four years, were collected. These data were utilized to estimate and compare the performance of the six machine learning techniques by adding technical and sentiment indicators to the price data along with the volume of posts. An accuracy of 90.57% and an area under the receiver operating characteristic curve value (AUC) of 97.48% were obtained using the extreme gradient boosting (XGBoost). It was shown that the use of both sentiment index using valence aware dictionary and sentiment reasoner (VADER) and 11 technical indicators utilizing moving average, relative strength index (RSI), stochastic oscillators in predicting Bitcoin price trends can produce significant results. Thus, the input features used in the paper can be applied on Bitcoin price prediction. Furthermore, this approach allows investors to make better decisions regarding Bitcoin-related investments.

show abstract

Section: Machine Learning For Predictionmentioning

confidence: 99%

Predicting Bitcoin Trends Through Machine Learning Using Sentiment Analysis with Technical Indicators

Jung¹,

Lee²,

Lee³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Table 1 shows a summary of related research conducted in diverse domains. Lee et al [13] used algorithms to predict customer satisfaction for mobile health services and demonstrated that extreme gradient boosting (XGBoost) can be adopted to forecast users' sentiments. To predict sentiment using restaurant feedback on quality and price, Zahoor et al [14] confirmed that Random Forest algorithm has higher accuracy than other algorithms.…”

Section: Applications Of Machine Learning In Various Domainsmentioning

confidence: 99%

Robust Sentiment Classification of Metaverse Services Using a Pre-trained Language Model with Soft Voting

2023

KSII TIIS

View full text Add to dashboard Cite

Metaverse services generate text data, data of ubiquitous computing, in real-time to analyze user emotions. Analysis of user emotions is an important task in metaverse services. This study aims to classify user sentiments using deep learning and pre-trained language models based on the transformer structure. Previous studies collected data from a single platform, whereas the current study incorporated the review data as "Metaverse" keyword from the YouTube and Google Play Store platforms for general utilization. As a result, the Bidirectional Encoder Representations from Transformers (BERT) and Robustly optimized BERT approach (RoBERTa) models using the soft voting mechanism achieved a highest accuracy of 88.57%. In addition, the area under the curve (AUC) score of the ensemble model comprising RoBERTa, BERT, and A Lite BERT (ALBERT) was 0.9458. The results demonstrate that the ensemble combined with the RoBERTa model exhibits good performance. Therefore, the RoBERTa model can be applied on platforms that provide metaverse services. The findings contribute to the advancement of natural language processing techniques in metaverse services, which are increasingly important in digital platforms and virtual environments. Overall, this study provides empirical evidence that sentiment analysis using deep learning and pre-trained language models is a promising approach to improving user experiences in metaverse services.

show abstract

“…Furthermore, lack of standardized frameworks for disclosing ESG-related metrics significantly intensifies the challenge of ensuring fairness and credibility [3,4]. Considering the escalating volume of data in the ESG domain, the growing importance of advanced natural language processing (NLP) techniques is evident in the effective management and investigation of the comprehensive corpus of unstructured textual data [5,6]. In an endeavor to address these complex challenges, this research engages in the task of proposing a strategy for the objective assessment of corporate open data's ESG scores utilizing automated text-based methodologies.…”

Section: Introductionmentioning

confidence: 99%

CORRECT? CORECT!: Classification of ESG Ratings with Earnings Call Transcript

2024

KSII TIIS

View full text Add to dashboard Cite

While the incorporating ESG indicator is recognized as crucial for sustainability and increased firm value, inconsistent disclosure of ESG data and vague assessment standards have been key challenges. To address these issues, this study proposes an ambiguous text-based automated ESG rating strategy. Earnings Call Transcript data were classified as E, S, or G using the Refinitiv-Sustainable Leadership Monitor's over 450 metrics. The study employed advanced natural language processing techniques such as BERT, RoBERTa, ALBERT, FinBERT, and ELECTRA models to precisely classify ESG documents. In addition, the authors computed the average predicted probabilities for each label, providing a means to identify the relative significance of different ESG factors. The results of experiments demonstrated the capability of the proposed methodology in enhancing ESG assessment criteria established by various rating agencies and highlighted that companies primarily focus on governance factors. In other words, companies were making efforts to strengthen their governance framework. In conclusion, this framework enables sustainable and responsible business by providing insight into the ESG information contained in Earnings Call Transcript data.

show abstract

Predicting User Satisfaction of Mobile Healthcare Services Using Machine Learning

Cited by 6 publications

References 63 publications

Predicting Bitcoin Trends Through Machine Learning Using Sentiment Analysis with Technical Indicators

Predicting Bitcoin Trends Through Machine Learning Using Sentiment Analysis with Technical Indicators

Robust Sentiment Classification of Metaverse Services Using a Pre-trained Language Model with Soft Voting

CORRECT? CORECT!: Classification of ESG Ratings with Earnings Call Transcript

Contact Info

Product

Resources

About