An Intelligent Online Shopping Guide Based On Product Review Mining

This position paper describes an on-going work on a novel recommendation framework for assisting online shoppers in choosing the most desired products, in accordance with requirements input in natural language. Existing feature-based Shopping Guidance Systems fail when the customer lacks domain expertise. This framework enables the customer to use natural language in the query text to retrieve preferred products interactively. In addition, it is intelligent enough to allow a customer to use objective and subjective terms when querying, or even the purpose of purchase, to screen out the expected products

The role of badges to spur frequent travelers to write online reviews

Purpose: Online travel reviews platforms have become innovative information systems also due to the incorporation of sophisticated gamification elements such as visually appealing badges. This study aims to analyze three features of the review after leveling up a badge: review length (number of words), sentiment scoring, and period between two successive reviews (number of days until the next review is written). Design/methodology/approach: A total of 77k online TripAdvisor reviews written by 100 frequent travelers and contributors are analyzed using a data mining approach. A data-based sensitivity analysis (DSA) is then conducted to provide an understanding of the data mining trained models. Findings: The results show evidence that badges appealing for self-pride (“badge passport”) and for peer-recognition (“badge helpful”) have significant influence across the lifespan of online review, whereas badges simply awarded by counting the contributions have little effect. Originality: This study provides the first analysis of how an experienced traveler is influenced as the badges and points are being awarded. Intrinsic motivational factor to award badges for standard contributions scarcely influence user behavior. Badges need to be designed to reward accomplishments that are not so trivial to be achieved and that do not depend entirely on the user.info:eu-repo/semantics/acceptedVersio

Understanding evolution of customers' expectations on Finnish Hotels

Hotel industry is a special characterized industry which is immaterial, non-storable, non-transportable and always include integration of external factors. Therefore, worth of mouth (WOM) or electronic-WOM is considered the most important reference in guests’ decision making process of hotel brand selection. Thanks to the development of user-generated-content (UGC) platform, guest users have been expanding their roles from information receivers to active content creators. That makes the voice of customers more remarkable and crucial than ever. Although many studies have been conducted in understanding customer behavior, there are gaps between customer expectation and hotelier perspective. The purpose of this study was to investigate online reviews from the guests of Helsinki hotels in order to identify their evolving expectations. Customer expectations on hotel service are believed to be evolving with time. Nonetheless, there is a lack of studies investigating how hotel customers’ expectations evolve with time. In this vein, this thesis investigated the changes in the most important topics and their related keywords that are manifested in online hotel reviews at different years. This study employed keywords extraction and sentiment analysis approaches pertaining to the methods such as POS tagging, N-gram and word frequency analysis. This research offers both academic and practical implications. For academics, the mining framework can be applied in many different industries. This can be considered as the antecedence of further automatic mining model such as co-occurrence analysis. Practically, the findings confirm most important hotel attributes such as “room” “breakfast” “location” “staff”, “cleanliness”, etc. The results revealed some interesting changes in customer expectations on hotel service. For instance example, new keyword “wifi” is replacing the presentation of old keywords “tv” and “internet”. These replacement prove the clear evolution of customer expectation that need to be concentrated by hotelier

A NOVEL FRAMEWORK BASED ON WORD-OF-MOUTH MINING FOR NON-PROSUMER DECISION SUPPORT

The deeper penetration of business-to-consumer e-commerce requires that customer decision support systems (CDSS) serve a wider range of users. However, a significant weakness of existing e-shopping assistance programs is their inability to aid non-professional consumers (non-prosumers) in buying highly differentiated products. This paper proposes a novel framework that infers product recommendations with minimal information input. At the heart of the proposed framework is the feature-usage map (FUM), a Bayesian network-based model that encodes the correlations among a product’s technical specifications and its suitability in terms of its using scenario (usage). It also incorporates a query-based lazy learning mechanism that elicits a product’s rating score from product reviews and constructs its corresponding FUM in an on-demand manner. This mechanism allows the knowledge base to be enriched incrementally, with no need for an exhaustive repository of FUMs pertaining to all possible usage queries a user may invoke. The effectiveness of the proposed framework is evaluated through an empirical user study. The results show that the framework is able to effectively derive product ratings based on specified usage. Moreover, this rating information can also be incorporated into a conventional buying guide system to deliver purchase decision support for non-prosumer

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

Destination image online analyzed through user generated content: a systematic literature review

Destination Image is a concept that has been studied for a long time in tourism research. The question of how a destination is perceived by tourists and potential new guests is an important insight, especially for local tourism managers, in order to evaluate the implemented strategies and to plan further tactics. Since the last two decades, due to a drastic digitalization, tourism research is now increasingly examining the Destination Image online. This creates new challenges in the selection of sources, methods, and in data collection. The aim of the present study was to systematically capture the approach to analyze the online Destination Image through User Generated Content using studies from the last ten years. Therefore, a Systematic Literature Review on primary research from academic databases was conducted. As a summary of the findings, a conceptual model was developed, based on the insights of the studies in the dataset, to contribute a guidance for the preparation phase of future online Destination Image research. In short, the main findings are: TripAdvisor.com is the main source for online Destination Image analysis. Researchers recommend using the help of software and programming languages to collect and analyzed the data. Equally to earlier Destination Image studies, the main methods applied in online Destination Image analysis are quantitative content analysis, qualitative content analysis and sentiment analysis. In combination with the examination of cognitive and affective factors, co-occurrence analysis, and correlation analysis. The present study has several limitations, which are: the loss of detail information due to reducing the studies to comparable key parameters, the absence of Anglo-American studies, due to the database selection as well as the lack of quality testing of the studies included.A Destination Image é um conceito que tem sido estudado há muito tempo na investigação turística. A questão de como o destino é visto pelos turistas e pelos potenciais novos hóspedes é uma perspectiva importante, especialmente para os gestores de turismo da região, a fim de avaliar as estratégias implementadas e de planear novas tácticas. Desde as últimas duas décadas, ocorreu uma digitalização drástica, a investigação turística adaptou-se a este fenómeno e está agora a estudar cada vez mais a imagem do destino online. Esta alteração criou novos desafios na selecção de fontes, métodos, e na recolha de dados. O objetivo do presente trabalho foi o de captar, de forma sistemática, as abordagens consideradas para analisar a imagem do destino online utilizando estudos dos últimos dez anos. Para este efeito, os estudos primários dos anos 2010-2020 das bases de dados académicos Web of Science, ProQuest e b-on, foram recolhidos utilizando palavras-chave de pesquisa pré-definidas. O grupo de artigos obtidos como resultado foram subsequentemente sujeitos a avaliação de eligibilidade, como recomendado por Moher et al. (2009). Isto significa que os estudos que não cumpriam os critérios pré-definidos foram excluídos. Os critérios de inclusão foram: O trabalho académico tinha de ser uma referência primária de uma revista científica, escrita em inglês e a amostra analisada tinha de ter uma origem associada à comunicação nas social media online. Posteriormente, os restantes 35 artigos foram transferidos para uma base de dados utilizando uma matriz de codificação. A matriz de codificação foi concebida para capturar os parâmetros-chave de cada estudo primário de uma forma padronizada e, portanto, comparável. Foi considerada informação geral, como o ano, localização e revista publicada, bem como informação temática específica, como o campo do turismo pesquisado e os meios analisados, juntamente com as categorias referentes à metodologia considerada, as ferramentas utilizadas e os resultados obtidos. A base de dados resultante foi então utilizada para obter declarações sobre a abordagem metodológica utilizada na análise da imagem de destinos online. Como resumo dos resultados, foi desenvolvido um modelo conceptual, baseado nos conhecimentos obtidos a partir do grupo de artigos, que constituiu o conjunto de dados para análise, para contribuir com um guião para a fase de preparação de uma futura investigação sobre imagem dos destinos online. Em resumo, as principais conclusões são: TripAdvisor.com é a principal fonte para a análise da imagem de destinos online. Os investigadores recomendam a utilização da ajuda de software e linguagens de programação para a recolha e análise dos dados. À semelhança de estudos anteriores de Destination Image, os principais métodos aplicados na análise imagem dos destinos online são a análise quantitativa do conteúdo, a análise qualitativa do conteúdo e a análise dos sentimentos. Em combinação com a análise dos fatores cognitivos e afectivos, análise de co-ocorrência, e análise de correlação. O presente estudo tem várias limitações. Que são: a perda de informação detalhada devido à redução dos estudos a parâmetros-chave comparáveis, a ausência de estudos anglo-americanos, devido à selecção do banco de dados, bem como a falta de testes de qualidade dos estudos incluídos.(TurExperience - Tourist experiences' impacts on the destination image: searching for new opportunities to the Algarve”)

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System