Mining Comparison Opinions from Chinese Online Reviews for Restaurant Competitive Analysis

Comparison is widely used by consumers during the process of product evaluation in order to emphasize their preference, which can contribute to a proxy for product competitiveness analysis. This paper proposes a novel method for mining comparative sentences based on the achievements of linguistic study. The definition of comparative sentence subcategory is put forward and a mixed rule pool containing both artificial rules and CSR is set up. Besides, an entity dictionary is used to re-check the identification result which can ensure precise identification and classification of comparative sentences. Real online comments are collected from Dianping.com as experimental data. The result shows that the proposed method outperforms baseline methods in terms of identification precision. Based on the result, features and opinions of comparative sentences are mined. We then conducted sentiment analysis to calculate the sentimental score of comparison relations. Finally, a feature competitive network of restaurants is constructed

Cross-Cultural Examination on Content Bias and Helpfulness of Online Reviews: Sentiment Balance at the Aspect Level for a Subjective Good

Online reviews can be fraught with biases, especially on experience goods. Using multilingual sentiment analysis software, we examined the characteristics of review biases and helpfulness at the aspect level across two different cultures. First, we found the lopsidedness of emotions expressed over the four key aspects of Japanese restaurant reviews between Japanese and Western consumers. Second, helpful reviews have sentiments expressed more evenly over those aspects than average for both Japanese and Western consumers. Third, however, there are significant differences over how sentiments are spread over aspects between them. Westerners found reviews helpful when reviews focused less on food and more on service. In addition, Japanese customers were more concerned with savings whereas Westerners paid attention to whether they are getting their money’s worth. These findings point to future research opportunities for leveraging sentiment analysis over key aspects of goods, particularly those of experience/subjective goods, across different cultures and customer profile categories

Destination image analytics through traveller-generated content

The explosion of content generated by users, in parallel with the spectacular growth of social media and the proliferation of mobile devices, is causing a paradigm shift in research. Surveys or interviews are no longer necessary to obtain users' opinions, because researchers can get this information freely on social media. In the field of tourism, online travel reviews (OTRs) hosted on travel-related websites stand out. The objective of this article is to demonstrate the usefulness of OTRs to analyse the image of a tourist destination. For this, a theoretical and methodological framework is defined, as well as metrics that allow for measuring different aspects (designative, appraisive and prescriptive) of the tourist image. The model is applied to the region of Attica (Greece) through a random sample of 300,000 TripAdvisor OTRs about attractions, activities, restaurants and hotels written in English between 2013 and 2018. The results show trends, preferences, assessments, and opinions from the demand side, which can be useful for destination managers in optimising the distribution of available resources and promoting sustainability

A study on text-score disagreement in online reviews

In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understanding the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that 1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa); and 2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from Tripadvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that -on a scale of five stars- those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews -on an initial very large dataset- that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement, which conveniently convey to the user a summary of positive and negative features of the review target.Comment: This is the accepted version of the paper. The final version will be published in the Journal of Cognitive Computation, available at Springer via http://dx.doi.org/10.1007/s12559-017-9496-

Information Technology Applications in Hospitality and Tourism: A Review of Publications from 2005 to 2007

The tourism and hospitality industries have widely adopted information technology (IT) to reduce costs, enhance operational efficiency, and most importantly to improve service quality and customer experience. This article offers a comprehensive review of articles that were published in 57 tourism and hospitality research journals from 2005 to 2007. Grouping the findings into the categories of consumers, technologies, and suppliers, the article sheds light on the evolution of IT applications in the tourism and hospitality industries. The article demonstrates that IT is increasingly becoming critical for the competitive operations of the tourism and hospitality organizations as well as for managing the distribution and marketing of organizations on a global scale

Hotel customer segmentation and sentiment analysis through online reviews: An analysis of selected European markets [Segmentação de clientes hoteleiros e análise de sentimento através de avaliações online: uma análise de mercados europeus selecionados]

Oliveira, A. S., Renda, A. I., Correia, M. B., & António, N. (2022). Hotel customer segmentation and sentiment analysis through online reviews: An analysis of selected European markets [Segmentação de clientes hoteleiros e análise de sentimento através de avaliações online: uma análise de mercados europeus selecionados]. Tourism & Management Studies, 18(1), 29-40. https://doi.org/10.18089/tms.2022.180----------------------------------------------This paper is financed by National Funds provided by FCTFoundation for Science and Technology through project UIDB/04470/2020This study aims to verify how distinct markets evaluate hotels in the Algarve through the analysis of online reviews, in order to identify if satisfaction and dissatisfaction attributes are similar among some of the main markets of overnight stay tourists in the region. Online reviews of hotels in the Algarve, written in English, French as well as Portuguese and posted on Tripadvisor by British, French and Portuguese residents from January 2019 to December 2019 are analysed. After the analysis of 8,596 online textual reviews, the results demonstrated that not only satisfaction and dissatisfaction rates towards hotel attributes differ according to the language, but also that customers from different countries place dissimilar emphasis on hotel attributes. Besides extending the current research on the use of online reviews, the findings of this study also assist hoteliers to identify improvement opportunities. Although many studies on marketing segmentation through data mining have been conducted, this paper analyses the customer satisfaction of relevant tourist markets and suggests up-to-date practical implications for hoteliers. --------------------------------------------------------------------------------------- Este estudo tem como objetivo verificar como mercados distintos avaliam hotéis no Algarve através da análise de comentários online, a fim de identificar se os atributos de satisfação e insatisfação são semelhantes entre alguns dos principais mercados turísticos da região. São analisadas avaliações online de hotéis no Algarve, escritas em inglês, francês e português e publicadas no Tripadvisor por residentes britânicos, franceses e portugueses de janeiro de 2019 a dezembro de 2019. Após a análise de 8.596 avaliações textuais online, os resultados demonstraram que não apenas as taxas de satisfação e insatisfação em relação aos atributos hoteleiros diferem de acordo com a língua, mas também que clientes de diferentes países colocam ênfase diferente nos atributos do hotel. Além de ampliar a pesquisa atual sobre o uso de revisões online, os resultados deste estudo também auxiliam os hoteleiros a identificar oportunidades de melhoria. Embora muitos estudos sobre segmentação de marketing por meio da mineração de dados tenham sido realizados, este artigo analisa a satisfação dos clientes dos mercados turísticos relevantes e sugere implicações práticas atualizadas para os hoteleirospublishersversionpublishe

Exploring Tourist Dining Preferences Based on Restaurant Reviews

Dining is an essential tourism component that attracts significant expenditure from tourists. Tourism practitioners need insights into the dining behaviors of tourists to support their strategic planning and decision making. Traditional surveys and questionnaires are time consuming and inefficient in capturing the complex dining behaviors of tourists at a large scale. Thus far, the understanding about the dining preferences and opinions of different tourist groups is limited. This article aims to fill the void by presenting a method that utilizes online restaurant reviews and text processing techniques in analyzing the dining behaviors of tourists. The effectiveness of the proposed method is demonstrated in a case study on international tourists visiting Australia using a large-scale data set of more than 40,000 restaurant reviews made by tourists on 2,265 restaurants. The proposed method can help researchers gain comprehensive insights into the dining preferences of tourists. </jats:p