How to monitor and generate intelligence for a DMO from online reviews

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Marketing IntelligenceSocial media and customer review websites have changed the way the tourism sector is managed. Social media has become a new source of information, due to the large amount of UGC / e-Wom generated by consumers An information that is "available" but at the same time noisy and of great volume, which makes it difficult to access and analyze. This study investigates and verifies the possibility of using data present in content reviews of a Content Web Site Review - TripAdivsor - to generate actionable information for a Destination Management Organization. With a focus on negative reviews, tourist attractions of Lisbon and using the “R code” and its packages, the study shows that with the correct technique chosen and the action of an intelligence analyst, data can be extracted and provide substrate for actions, strategy and intelligence generation – which is Social Media Intelligence. The findings prove that the flood of web 2.0 data can serve as a source of intelligence for the Destination Management Organization (DMO). By monitoring sites like TripAdvisor, a DMO can hear what tourists talk about attractions and thereby generate insights for intelligence and strategy actions. A DMO can even, analyzing this data, make your attractions more desirable, and even act in adverse situations, reducing risky situations

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

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

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Blogging mastery: analyzing the key strategies behind successful blogs

Bloggers in the digital landscape have the power to shape consumer behavior and influence their peers. However, successfully running a blog demands time and commitment, similar to operating a small business. Yet, there is scant literature regarding successful practices and strategies that bloggers use to build their blogs and remain successful. This study explores bloggers\u27 most effective methods and strategies to establish themselves in their respective niches. The qualitative research study uses transcendental phenomenology to examine the lived experiences of successful bloggers, aiming to provide insights into their successful strategies, best practices, challenges, and insights for new bloggers. Twelve bloggers that met the criteria for inclusion were interviewed using 12 semi-structured open-ended questions. Thematic analysis was used to code and categorize the themes. The findings suggest that bloggers use various strategies to establish themselves in their respective niches and overcome challenges. The study results were integrated and used to develop the Blogger Success Framework to help established and aspiring bloggers navigate the digital landscape of blogging

Management Responses to Online Reviews: Big Data From Social Media Platforms

User-generated content from virtual communities helps businesses develop and sustain competitive advantages, which leads to asking how firms can strategically manage that content. This research, which consists of two studies, discusses management response strategies for hotel firms to gain a competitive advantage and improve customer relationship management by leveraging big data, social media analytics, and deep learning techniques. Since negative reviews' harmful effects are greater than positive comments' contribution, firms must strategise their responses to intervene in and minimise those damages. Although current literature includes a sheer amount of research that presents effective response strategies to negative reviews, they mostly overlook an extensive classification of response strategies. The first study consists of two phases and focuses on comprehensive response strategies to only negative reviews. The first phase is explorative and presents a correlation analysis between response strategies and overall ratings of hotels. It also reveals the differences in those strategies based on hotel class, average customer rating, and region. The second phase investigates effective response strategies for increasing the subsequent ratings of returning customers using logistic regression analysis. It presents that responses involving statements of admittance of mistake(s), specific action, and direct contact requests help increase following ratings of previously dissatisfied returning customers. In addition, personalising the response for better customer relationship management is particularly difficult due to the significant variability of textual reviews with various topics. The second study examines the impact of personalised management responses to positive and negative reviews on rating growth, integrating a novel method of multi-topic matching approach with a panel data analysis. It demonstrates that (a) personalised responses improve future ratings of hotels; (b) the effect of personalised responses is stronger for luxury hotels in increasing future ratings. Lastly, practical insights are provided

Some Advances in Aspect Analysis of User-Generated Content

Starting from the online reviews associated with an overall rating, the aim is to propose a methodology for detecting the main aspects (or topics) of interest for users, and afterwards to estimate the aspect ratings latently assigned in each review jointly with the weight or emphasis put on each aspect

A design theory for requirements mining systems

Software requirements are often communicated in unstructured text documents, which need to be analyzed in order to identify and classify individual needs. This process is referred to as requirements mining in the context of this thesis. It is known to be time-consuming and error-prone when performed manually by a requirements engineer. Thus, there is a demand to support requirements mining through information technology. However, little research has been conducted to conceptualize theoretically grounded requirements mining systems and abstract the necessary design knowledge in a theory. Furthermore, existing works scarcely investigate the effect of these artifacts on requirements engineers’ productivity. Consequently in this thesis, the following research question is addressed: How can a system be designed which aims at improving requirements mining productivity over manual discovery? Following a Design Science approach, a design theory is derived consisting of design requirements, design principles and design features. Design requirements are identified based on general knowledge and kernel theories. Subsequently they are related to design principles which are finally mapped to design features of an artifact. The artifact is conceptualized in two design cycles, each resulting in a distinct artifact version and its evaluation. In the first design cycle a simulation is conducted to investigate the interplay of the preliminary design principles. In the second design cycle, the effects of the final design principles on requirements mining productivity are measured in an experiment. The thesis contributes to the design theory body of knowledge by providing a design theory for requirements mining systems. The theory is a contribution to the information systems literature because requirements mining systems represent an important class of design situations that have not been adequately described yet by existing works. From a practical point of view, the study addresses the need of requirements engineers to support their work by information technology and provides vendors of requirements engineering software packages guidelines to improve their products