Tourism-Related Opinion Mining

This paper focuses on the tourism-related opinion mining, including tourism-related opinion detection and tourist attraction target identification. The experimental data are blog articles labeled as in the domestic tourism category in a blogspace. Annotators were asked to annotate the opinion polarity and the opinion target for every sentence. Different strategies and features have been proposed to identify opinion targets, including tourist attraction keywords, coreferential expressions, tourism-related opinion words, a 2-level classifier, and so on. We used machine learning methods to train classifiers for tourism-related opinion mining. A retraining mechanism was proposed to obtain the system decisions of preceding sentences as a new feature. The precision and recall scores of tourism-related opinion detection were 55.98% and 59.30%, respectively, and the scores of tourist attraction target identification among known tourism-related opinionated sentences were 90.06% and 89.91%, respectively. The overall precision and recall scores were 51.30% and 54.21%, respectively.