TripAdvisor Review Sentiment Mining
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.
Catatan sumber
Kutipan disalin apa adanya dari catatan sumber metode. Tidak ada verifikasi tingkat klaim yang disimpulkan darinya.
- Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. · DOI 10.1561/1500000011
- Xiang, Z., Schwartz, Z., Gerdes, J. H., & Uysal, M. (2015). What can big data and text analytics tell us about hotel guest experience and satisfaction? International Journal of Hospitality Management, 44, 120-130. · DOI 10.1016/j.ijhm.2014.10.013
Klaim yang dikurasi
Klaim tersimpan dalam buku besar bukti, masing-masing dengan penilaiannya sendiri.
Tampilan ini tidak menciptakan penilaian klaim ketika buku besar tidak memilikinya.
Metode terkait
Dihasilkan dari grafik metode dan ditampilkan sebagai relasi yang disarankan mesin — tidak ada klaim bukti yang disimpulkan.