Tourism-Related Opinion Detection and Tourist-Attraction Target Identification

Computational Linguistics and Chinese Language Processing Vol. 15, No. 1, March 2010, pp. 37-60 37 © The Association for Computational Linguistics and Chinese Language Processing Tourism-Related Opinion Detection and Tourist-Attraction Target Identification Chuan-Jie Lin∗ and Pin-Hsien Chao∗ Abstract This paper focuses on tourism-related opinion mining, including tourism-related opinion detection and tourist-attraction target identification. The experimental data are blog articles labeled as being 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, and a 2-level classifier. We used machine learning methods to train classifiers for tourism-related opinion mining. A retraining mechanism is proposed to obtain the system decisions of preceding sentences. 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. Keywords: Tourism-Related Opinion Mining, Tourist Attraction Target Identification, Opinion Analysis. 1. Introduction The blogspace is a large resource for opinion mining. Opinion extraction methods are valuable for a wide range of applications. Our initial interest is to extract opinions related to tourist attractions from blog articles because it is helpful to see other people’s opinions about tourist attractions when planning a tour. Nevertheless, two issues arise when trying to apply published methods to retrieve opinions of tourist attractions: ∗ Department of Computer Science and Engineering, National Taiwan Ocean University No 2, Pei-Ning Road, Keelung, 20224 Taiwan E-mail: {cjlin, M96570038}@ntou.edu.tw 38 Chuan-Jie Lin and Pin-Hsien Chao (1) Sentence-level or document-level: A travel article is often multi-topic because a travel route often includes several tourist attractions. Therefore, the opinion analysis for a specific tourist attraction should be carried out at sentence level, not document level. (2) Opinion topic or opinion target: Tourist attractions may be treated as topics (queries in IR) or as targets of opinions. Consider the following two sentences selected and adapted from our dataset: The Dream Lake is a beautiful place. The water is green and clear. Both sentences are considered tourism-related opinions by us. Their opinion targets, however, are not the same. The opinion target of the first sentence is “the Dream Lake” itself, while the target of the second sentence is “the water” (in the Dream Lake). Both sentences are related to the same topic, “the Dream Lake,” but the second sentence does not contain its topic words. We find difficulty in applying the previously developed methods due to these reasons. Opinion mining and analysis have been widely studied in several topics, including opinion detection and polarity classification (Wiebe et al., 2001; Pang et al., 2002; Alm et al., 2005; Ghose et al., 2007), opinion holder finding (Choi et al., 2005; Kim & Hovy, 2005; Breck et al., 2007), and opinion summarization (Ku et al., 2005). Some well-known large-scale opinion …

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