The purpose of this study is to conduct topic modeling and sentiment analysis on the posts of Skytrax (airlinequality.com), where there are many interests and participation of the people who have used or are willing to use it for airlines. The purpose of people gathering at Skytrax is to make better choices using the actual experiences of other customers who have experienced airlines. Online reviews written by customers with experience using airlines in Asia were collected. The data collected were online reviews from 27 airlines, with more than 14,000 reviews. Topic modeling and sentiment analysis were used with the collected data to figure out what kinds of important words are in the online reviews. As a result of the topic modeling, ‘seat’, ‘service’, and ‘meal’ were significant issues in the flight through frequency analysis. Additionally, the result revealed that delay was the main issue, which can affect customer dissatisfaction while ‘staff service’ can make customers satisfied through sentiment analysis as the result shows the ‘staff service’ with meal and food in the topic modeling.

https://www.mdpi.com/2078-2489/12/2/78/pdf

Topic Modeling and Sentiment Analysis of Online Review for Airlines

Exploring China's 5A global geoparks through online tourism reviews: A mining model based on machine learning approach

Blockchain for business management: Applications, challenges and potentials

The purpose of this study is to thoroughly review studies that have used blockchain technology in financial services. This study will help provide a holistic framework that would highlight the current state and challenges of the blockchain in the financial services sector.,The objective of this study is to systematically examine and organize the current body of research literature that either quantitatively or qualitatively explored the use of blockchain technology in financial services. The study uses PRISMA-guided systematic review along with bibliometric analysis to achieve the purpose.,This study contributes to the existing literature by exploring and analyzing systematic studies available on blockchain with special reference to financial services sector. With blockchain based on five principles, namely, computational logic, peer-to-peer transmission, irreversibility of records, distributed database and transparency with pseudonym has immense potential to unleash and transform the financial service industry. With increasing blockchain-based operations of decentralized banking, insurance, trade finance, financial markets and cryptocurrency market, the subject is rapidly growing and seeking considerable contribution from scholars from around the world.,This study uses systematic literature review approach, which has its own demerits. Like other studies based on Systematic Literature Review, this study also suffers from a certain bias such as sample selection bias, publication bias, data interpretation and the combination of quantitative and qualitative studies in the population. Further, the adoption and resultant benefits of blockchain have not been empirically tested.,This study can help policymakers and institutions in determining their future course of action, as it highlights the state of research in the area of blockchain technology and financial services.,Very few studies have done a comprehensive review of literature on blockchain in financial services.

Blockchain technology in financial services: a comprehensive review of the literature

The use of blockchain technology leads to improved operations and supply chain (SC) integration. Moreover, identifying and evaluating the blockchain technology application criteria in the logistics system is a Multi-Criteria Decision-Making (MCDM) challenge that requires taking into account the perspectives of experts with varying degrees of SC expertise. The purpose of this paper is to propose a novel group decision-making method based on the best-worst method (BWM) to evaluate the criteria of implementing blockchain technology in the SC. The proposed approach provides a mechanism whereby opinions of decision-makers (DMs) are aggregated in nine steps, first. Then, weights of criteria are determined using BWM individual decision-making models. Moreover, two individual decision-making methods called nonlinear goal programming based BWM II (NGPBWM II) and linear goal programming based BWM II (LGPBWM II) are extended in this study, which can be adopted in both individual and group decision-making problems. The NGPBWM II and LGPBWM II methods can be used for both individual and group decision-making. This study proposes a novel group decision-making framework. The framework has fewer constraints than previous group BWM models and can consider different best (worst) criteria by different DMs. The effectiveness of the proposed methodology is investigated employing eight numerical examples. The results reveal the high accuracy of the NGPBWM II in all eight examples. Therefore, a combination of the proposed group decision-making method and NGPBWM II is applied in a real case to evaluate the application criteria of the blockchain technology in the automotive industry SC, which was a new application for using blockchain technology in SC. 

A novel group BWM approach to evaluate the implementation criteria of blockchain technology in the automotive industry supply chain

Blockchain has become one of the core technologies in Industry 4.0. To help decision-makers establish action plans based on blockchain, it is an urgent task to analyze trends in blockchain technology. However, most of existing studies on blockchain trend analysis are based on effort demanding full-text investigation or traditional bibliometric methods whose study scope is limited to a frequency-based statistical analysis. Therefore, in this paper, we propose a new topic modeling method called Word2vec-based Latent Semantic Analysis (W2V-LSA), which is based on Word2vec and Spherical k-means clustering to better capture and represent the context of a corpus. We then used W2V-LSA to perform an annual trend analysis of blockchain research by country and time for 231 abstracts of blockchain-related papers published over the past five years. The performance of the proposed algorithm was compared to Probabilistic LSA, one of the common topic modeling techniques. The experimental results confirmed the usefulness of W2V-LSA in terms of the accuracy and diversity of topics by quantitative and qualitative evaluation. The proposed method can be a competitive alternative for better topic modeling to provide direction for future research in technology trend analysis and it is applicable to various expert systems related to text mining.

https://scholarworks.unist.ac.kr/bitstream/201301/32345/1/000532801200024.pdf

Haecheong Park

Papers

Word2vec-based latent semantic analysis (W2V-LSA) for topic modeling: A study on blockchain technology trend analysis