Identifying Customer Preferences about Tourism Products Using an Aspect-based Opinion Mining Approach

AbstractIn this study we extend Bing Liu's aspect-based opinion mining technique to apply it to the tourism domain. Using this extension, we also offer an approach for considering a new alternative to discover consumer preferences about tourism products, particularly hotels and restaurants, using opinions available on the Web as reviews. An experiment is also conducted, using hotel and restaurant reviews obtained from TripAdvisor, to evaluate our proposals. Results showed that tourism product reviews available on web sites contain valuable information about customer preferences that can be extracted using an aspect-based opinion mining approach. The proposed approach proved to be very effective in determining the sentiment orientation of opinions, achieving a precision and recall of 90%. However, on average, the algorithms were only capable of extracting 35% of the explicit aspect expressions

A Survey on Mining Top-k Competitors from Large Unstructured Dataset Using k_means Clustering Algorithm and Sentiment Analysis Approach

Along line of research has shown the vital significance of recognizing and observing company�s contestants. In the framework of this activity various questions are emerge like: In what way we formalize and measure the competitiveness between two items? Who are the most important competitors of a specified item? What are the various features of an item that act on competitiveness? Inspired by this issue, the advertising and administration group have concentrated on observational strategies for competitor distinguishing proof and in addition on techniques for examining known contenders. Surviving examination on the previous has concentrated on mining near articulations (e.g.one product is superior then other product) from the web or other documentary sources. Despite the fact that such articulations can without a doubt be indications of strength, they are truant in numerous spaces. By surveying the various papers, we found the conclusion of basic significance of the competitiveness between two items on the basis of market segments

Implicit Sentiment Identification using Aspect based Opinion Mining

Opinion mining or sentiment analysis is the computational study of opinions or emotions towards aspects or things. The aspects are nothing but attributes or components of the individuals, events, topics, products and organizations. Opinion mining has been an active research area in Web mining and Natural Language Processing (NLP) in recent years. With the explosive growth of E-commerce, there are millions of product options available and people tend to review the viewpoint of others before buying a product. An aspect-based opinion mining approach helps in analyzing opinions about product features and attributes. This project is based on extracting aspects and related customer sentiments on tourism domain. This offers an approach to discover consumer preferences about tourism products and services using statistical opinion mining. The proposed system tries to extract both explicit aspects as well as implicit aspects from customer reviews. It thus increases the sentiment orientation of opinion. Most of the researches were based on explicit opinions of customers. This system tries to retrieve implicit sentiments. Due to the growing availability of unstructured reviews, the proposed system gives a summarized form of the information that is obtained from the reviews in order to furnish customers with pin point or crisp results. DOI: 10.17762/ijritcc2321-8169.16049

Social media competitive analysis and text mining: a case study in digital marketing in the hospitality industry

Objectives The main objectives of this study were to explore the effectiveness of using text mining to analyse the consumer generated content from online hotel reviews. Specifically, this study focuses on demonstrating the helpfulness of such tools in the case of Original Sokos Hotel Vaakuna Helsinki and Scandic Marski in Finland. By analyzing the current trends and patterns of the online reviews of the two hotels, the objective of the study is to understand the extent to which text mining can improve marketing decisions and thus bring value to consumers. Summary The tourism and hospitality industry has changed tremendously due to the emergence of online review platforms such as TripAdvisor.com. This study applies text mining analytics to conduct a content analysis on the social media content provided by hotel guests on these platforms. To gain competitive insights from the data, topic classification and sentiment analysis are used. Conclusions The findings of the research illustrate how topics and related sentiment can be identified from the online content. Although there are several similarities between the data regarding online discussion, the text mining analysis also identified some differences, which have the potential to contribute to gaining competitive intelligence in the industry. Overall, the study illustrates how simple text mining software, which requires little resources from firms can provide beneficial information about the market to hotels in international business

Assessment, Implication, and Analysis of Online Consumer Reviews: A Literature Review

The onset of e-marketplace, virtual communities and social networking has appreciated the influential capability of online consumer reviews (OCR) and therefore necessitate conglomeration of the body of knowledge. This article attempts to conceptually cluster academic literature in both management and technical domain. The study follows a framework which broadly clusters management research under two heads: OCR Assessment and OCR Implication (business implication). Parallel technical literature has been reviewed to reconcile methodologies adopted in the analysis of text content on the web, majorly reviews. Text mining through automated tools, algorithmic contribution (dominant majorly in technical stream literature) and manual assessment (derived from the stream of content analysis) has been studied in this review article. Literature survey of both the domains is analyzed to propose possible area for further research. Usage of text analysis methods along with statistical and data mining techniques to analyze review text and utilize the knowledge creation for solving managerial issues can possibly constitute further work. Available at: https://aisel.aisnet.org/pajais/vol9/iss2/4

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