Сравнение методов

	Travel Community Netnography	TripAdvisor Review Sentiment Mining
Область	Tourism	Tourism
Семейство	Process / pipeline	Machine learning
Год появления	2002	2008
Автор метода	Robert V. Kozinets	Bo Pang & Lillian Lee (opinion mining); applied to hotel reviews by Zheng Xiang and colleagues
Тип	Adapted ethnographic pipeline for studying online communities	Supervised/lexicon text-classification of review polarity and opinion
Основополагающий источник	Kozinets, R. V. (2002). The Field Behind the Screen: Using Netnography for Marketing Research in Online Communities. Journal of Marketing Research, 39(1), 61-72.	Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Другие названия	Online Travel Community Ethnography, Tourism Netnography, Travel Forum Netnographic Analysis, Digital Travel Community Ethnography	Online Hotel Review Sentiment Analysis, Travel Review Opinion Mining, Hospitality Review Polarity Classification, Tourism Review Sentiment Classification
Связанные	4	4
Сводка	Travel community netnography applies netnography, ethnography adapted to the study of online communities, to the forums, social-media groups, blogs and review communities where travellers gather to share experiences, advice and meaning. Developed by Robert Kozinets (2002, 2010), netnography offers a rigorous, ethically grounded set of procedures for entering an online community, immersing in its communications, and interpreting the symbolism, meanings and consumption practices of its members. In tourism it is used to understand how travellers construct destinations, make decisions, perform identity and form attachments through naturally occurring online interaction, observed unobtrusively rather than provoked by an interviewer. Kozinets positioned netnography as faster, less costly and more naturalistic than offline ethnography while demanding the same interpretive depth, reflexivity and ethical care.	TripAdvisor review sentiment mining applies opinion mining and sentiment analysis to the large volumes of online reviews that travellers write about hotels, restaurants and attractions on platforms such as TripAdvisor. Grounded in the opinion-mining methodology surveyed by Pang and Lee (2008), it uses lexicon-based or machine-learning text classifiers to determine whether a review, sentence or opinion is positive, negative or neutral, turning unstructured free text into structured sentiment data. Applied to hospitality, as demonstrated by Xiang and colleagues (2015) in their big-data analysis of hotel guest experience, the technique can go beyond an overall verdict to extract aspect-level sentiment, revealing how guests feel about specific facets like room, service, location, value and cleanliness. The result is a scalable way to read what thousands of guests are actually saying and to quantify the tone of a property's online reputation.