Compara mètodes

	TripAdvisor Review Sentiment Mining	Destination Net Promoter Analysis
Camp	Tourism	Tourism
Família	Machine learning	Process / pipeline
Any d'origen	2008	2003
Autor original	Bo Pang & Lillian Lee (opinion mining); applied to hotel reviews by Zheng Xiang and colleagues	Frederick Reichheld (Net Promoter Score); adapted to destination advocacy
Tipus	Supervised/lexicon text-classification of review polarity and opinion	Single-item recommendation-likelihood metric and advocacy-segmentation pipeline
Font seminal	Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Reichheld, F. F. (2003). The One Number You Need to Grow. Harvard Business Review, 81(12), 46-54.
Àlies	Online Hotel Review Sentiment Analysis, Travel Review Opinion Mining, Hospitality Review Polarity Classification, Tourism Review Sentiment Classification	Destination Advocacy Score, Destination Recommendation Index, Tourist Net Promoter Measurement, Destination Word-of-Mouth Likelihood Score
Relacionats	4	4
Resum	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.	Destination net promoter analysis adapts the Net Promoter Score, introduced by Frederick Reichheld (2003), to the measurement of destination advocacy. It rests on a single survey question, how likely a visitor is, on a 0-to-10 scale, to recommend the destination to a friend or colleague, and converts the answers into a compact indicator of word-of-mouth potential. Respondents are sorted into promoters, passives and detractors, and the score is the percentage of promoters minus the percentage of detractors. The metric is attractive for destinations because, as Litvin, Goldsmith and Pan (2008) emphasise, word-of-mouth is one of the most influential information sources in tourism, where intangible products are hard to judge before consumption; a destination's promoters become its advocates, spreading recommendations that drive future visitation, especially through electronic word-of-mouth.