What Airbnb Reviews can Tell us? An Advanced Latent Aspect Rating Analysis Approach

There is no doubt that the rapid growth of Airbnb has changed the lodging industry and tourists’ behaviors dramatically since the advent of the sharing economy. Airbnb welcomes customers and engages them by creating and providing unique travel experiences to “live like a local” through the delivery of lodging services. With the special experiences that Airbnb customers pursue, more investigation is needed to systematically examine the Airbnb customer lodging experience. Online reviews offer a representative look at individual customers’ personal and unique lodging experiences. Moreover, the overall ratings given by customers are reflections of their experiences with a product or service. Since customers take overall ratings into account in their purchase decisions, a study that bridges the customer lodging experience and the overall rating is needed. In contrast to traditional research methods, mining customer reviews has become a useful method to study customers’ opinions about products and services. User-generated reviews are a form of evaluation generated by peers that users post on business or other (e.g., third-party) websites (Mudambi & Schuff, 2010). The main purpose of this study is to identify the weights of latent lodging experience aspects that customers consider in order to form their overall ratings based on the eight basic emotions. This study applied both aspect-based sentiment analysis and the latent aspect rating analysis (LARA) model to predict the aspect ratings and determine the latent aspect weights. Specifically, this study extracted the innovative lodging experience aspects that Airbnb customers care about most by mining a total of 248,693 customer reviews from 6,946 Airbnb accommodations. Then, the NRC Emotion Lexicon with eight emotions was employed to assess the sentiments associated with each lodging aspect. By applying latent rating regression, the predicted aspect ratings were generated. With the aspect ratings, , the aspect weights, and the predicted overall ratings were calculated. It was suggested that the overall rating be assessed based on the sentiment words of five lodging aspects: communication, experience, location, product/service, and value. It was found that, compared with the aspects of location, product/service, and value, customers expressed less joy and more surprise than they did over the aspects of communication and experience. The LRR results demonstrate that Airbnb customers care most about a listing location, followed by experience, value, communication, and product/service. The results also revealed that even listings with the same overall rating may have different predicted aspect ratings based on the different aspect weights. Finally, the LARA model demonstrated the different preferences between customers seeking expensive versus cheap accommodations. Understanding customer experience and its role in forming customer rating behavior is important. This study empirically confirms and expands the usefulness of LARA as the prediction model in deconstructing overall ratings into aspect ratings, and then further predicting aspect level weights. This study makes meaningful academic contributions to the evolving customer behavior and customer experience research. It also benefits the shared-lodging industry through its development of pragmatic methods to establish effective marketing strategies for improving customer perceptions and create personalized review filter systems

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Airbnb customer satisfaction through online reviews

With the development and better access to the Internet, mobile devices and social media, people began to post online their opinions and reviews of products and services. These comments influence new customer buying decisions and qualify companies to gain superior insight into their customers’ experience and satisfaction. Thus, it has become essential for companies to adopt methods capable of analyzing this information and extracting its value in order to better serve their customers’ unmet needs. The area of tourism and hospitality was one of the most affected by this trend. For this reason, this study will focus on the reviews of an online platform, Airbnb, so that it also studies the technological disruption in the mentioned industry. This new method of home-sharing has gained more and more followers for its advantages and differences compared to common hotels, which has triggered increasing researcher. Airbnb’s guest reviews describe each guest’s experiences (the positive and negative aspects of their stay) and will be studied through Text Mining. This consists of several methods capable of analyzing large amounts of unstructured information such as Big Data, in order to better understand overall customer satisfaction, including the factors that will influence it. Results show that distinct dimensions are valued by guests and they are different in different areas of Sintra.Com o desenvolvimento e maior acesso à Internet, dispositivos móveis e redes sociais, as pessoas começaram a publicar online as suas opiniões e avaliações de produtos e serviços. Estes comentários influenciam as decisões de compra de novos clientes e permitem às empresas obter um maior conhecimento sobre a experiência e satisfação dos seus clientes. Assim, tornou-se imprescindível para as estas, adotarem métodos capazes de analisar esta informação e extrair valor da mesma de modo a conseguirem atender de forma mais ajustada às necessidades dos seus clientes. A área da hospitalidade foi uma das mais afetadas por esta tendência. Por esse motivo, este estudo vai ser focado nas reviews de uma plataforma online, o Airbnb, juntando assim também uma disrupção tecnológica desta mesma área. Este novo método de alojamento partilhado tem ganho cada mais seguidores pelas suas vantagens e diferenças em relação a hotéis mais comuns, mas também tem sido um assunto cada vez mais estudado por investigadores. Os comentários estudados do Airbnb descrevem as experiências de cada hóspede relativamente ao alojamento onde permaneceram e são estudados através de Text Mining. Este consiste em vários métodos capazes de analisar grandes volumes de informação não estruturados como Big data para consequentemente compreender melhor a satisfação geral dos clientes, nomeadamente os fatores que a vão influenciar. Os resultados mostram que existem várias dimensões valorizadas e diferentes para as zonas estudadas em Sintra

Capitalizing on Social Media Analysis – Insights from an Online Review on Business Models

With the rise and proliferation of social media on the Internet, social media analysis is emerging as a new business model for software companies. The purpose of this paper is to provide a systematic overview of different types of such business models. After developing a coding schema based on the business model, we conducted an in-depth analysis of 16 websites of companies that actively promote social media analysis to their clients. We identified three archetypes of business models in this area: specialist content analysts, social data and application integrator, and social media service provider. Future research can build on these insights in order to focus on designing or revising methods for social media analysis to realize either of these business models. Software companies can benefit from the results by positioning their own business models in this emerging market more thoughtfully

KID - an algorithm for fast and efficient text mining used to automatically generate a database containing kinetic information of enzymes

<p>Abstract</p> <p>Background</p> <p>The amount of available biological information is rapidly increasing and the focus of biological research has moved from single components to networks and even larger projects aiming at the analysis, modelling and simulation of biological networks as well as large scale comparison of cellular properties. It is therefore essential that biological knowledge is easily accessible. However, most information is contained in the written literature in an unstructured way, so that methods for the systematic extraction of knowledge directly from the primary literature have to be deployed.</p> <p>Description</p> <p>Here we present a text mining algorithm for the extraction of kinetic information such as K_M, K_i, k_catetc. as well as associated information such as enzyme names, EC numbers, ligands, organisms, localisations, pH and temperatures. Using this rule- and dictionary-based approach, it was possible to extract 514,394 kinetic parameters of 13 categories (K_M, K_i, k_cat, k_cat/K_M, V_max, IC₅₀, S_0.5, K_d, K_a, t_1/2, pI, n_H, specific activity, V_max/K_M) from about 17 million PubMed abstracts and combine them with other data in the abstract.</p> <p>A manual verification of approx. 1,000 randomly chosen results yielded a recall between 51% and 84% and a precision ranging from 55% to 96%, depending of the category searched.</p> <p>The results were stored in a database and are available as "KID the KInetic Database" via the internet.</p> <p>Conclusions</p> <p>The presented algorithm delivers a considerable amount of information and therefore may aid to accelerate the research and the automated analysis required for today's systems biology approaches. The database obtained by analysing PubMed abstracts may be a valuable help in the field of chemical and biological kinetics. It is completely based upon text mining and therefore complements manually curated databases.</p> <p>The database is available at <url>http://kid.tu-bs.de</url>. The source code of the algorithm is provided under the GNU General Public Licence and available on request from the author.</p

Tutorial: Legality and Ethics of Web Scraping

Researchers and practitioners often use various tools and technologies to automatically retrieve data from the Web (often referred to as Web scraping) when conducting their projects. Unfortunately, they often overlook the legality and ethics of using these tools to collect data. Failure to pay due attention to these aspects of Web Scraping can result in serious ethical controversies and lawsuits. Accordingly, we review legal literature together with the literature on ethics and privacy to identify broad areas of concern together with a list of specific questions that researchers and practitioners engaged in Web scraping need to address. Reflecting on these questions and concerns can potentially help researchers and practitioners decrease the likelihood of ethical and legal controversies in their work

the case of accommodation sharing

Thesis(Doctoral) --KDI School:Ph.D in Public Policy,2019The purpose of this study is to provide implications on policy preparation and amendments on laws and regulations in accommodation sharing in Korea by exploring the current status of demand and supply sides. This study consists of four parts to investigate i) perceived characteristics of accommodation sharing, ii) the impact of attributes of accommodations sharing on business performance, iii) individuals’ perceptions of policy reactions, and iv) exploratory research of current laws and regulations of different countries. First, this study finds that actual preferences of accommodation sharing conflicts with the issues on laws and regulations regarding property and sharing types. Guests who prefer to share entire houses consider instrumental attributes related to properties, while guests who prefer a portion of the house consider relatively more about social interactions, sustainability, and community benefit. Sharing a portion of the houses is legal and more suitable for policy intentions because the policies promote the local economy and community recovery by maximizing the utility of resources and interactions with the community. Further, this study finds that individuals with experience of accommodation sharing tend to have more positive attitudes toward accommodation sharing and perceive more necessity of policy reactions. Among proposed policy instruments, individuals perceive local ordinances, government publicizing and campaign, trust marks, taxation, penalties, and government controls are effective to build trust in accommodation sharing. Individuals evaluate that policies geared toward the majority of the public are more effective, and governments should establish a strategic approach as to which policies are introduced in public and which role the government plays in the departments. Currently, governments have been required the incompatible roles of eliminating regulatory barriers for newly introduced sharing economy business and minimizing the damages to existing industries. This study provides policy and managerial implications what is the most important for the citizen satisfaction associated with proper preparations and amendments of laws and regulations.I. Introduction II. Literature Reviews III. Study 1: Qualitative Research using Secondary Data IV. Study 2: Quantitative Research using Secondary Data V. Study 3: Quantitative Research using Primary Data VI. Study 4: Comparative Study on Policies in Various Societies VII. ConclusiondoctoralpublishedEun Joo LEE

Term-driven E-Commerce

Die Arbeit nimmt sich der textuellen Dimension des E-Commerce an. Grundlegende Hypothese ist die textuelle Gebundenheit von Information und Transaktion im Bereich des elektronischen Handels. Überall dort, wo Produkte und Dienstleistungen angeboten, nachgefragt, wahrgenommen und bewertet werden, kommen natürlichsprachige Ausdrücke zum Einsatz. Daraus resultiert ist zum einen, wie bedeutsam es ist, die Varianz textueller Beschreibungen im E-Commerce zu erfassen, zum anderen können die umfangreichen textuellen Ressourcen, die bei E-Commerce-Interaktionen anfallen, im Hinblick auf ein besseres Verständnis natürlicher Sprache herangezogen werden