Three equally experienced annotators provided sentence-level annotations of a subset of 500 randomly selected reviews from the publicly available TripAdvisor dataset1. The full TripAdvisor dataset consists of 235,793 hotel reviews crawled over a period of one month. In addition to the review text, each review comes with a hotel identifier, an overall rating and optional aspect-specific ratings for the following seven aspects: Rooms, Cleanliness, Value, Service, Location, Checkin, and Business. All review-level ratings are on a discrete ordinal scale from 1 to 5 (with -1 indicating that an aspect-specific rating was not provided by the reviewer).
The annotations are related to nine aspects typically mentioned in hotel reviews. In addition to the seven aspects explicitly present (at the review level) in the TripAdvisor dataset, we decided to add two other aspects (aspectFood and Building), since many comments in the reviews refer to them. Furthermore, the “catch-all” aspects Other and NotRelated were added, for a total of eleven aspects. Other captures those opinion-related aspects that cannot be assigned to any of the first nine aspects, but which are still about the hotel under review, for example, Extremely friendly place. The NotRelated aspect captures those opinion-related aspects that are not relevant to the hotel under review, as for example in The crew on our flight were wonderful. ￼￼￼￼￼
The annotation distinguishes between Positive, Negative and Neutral/Mixed opinions. The Neutral/Mixed label is assigned to opinions that are about an aspect without expressing a polarized opinion, and to opinions of contrasting polarities, such as The room was average size (neutral) and Pricey but worth it! (mixed). The annotations also distinguish between explicit and implicit opinion expressions, that is, between expressions that refer directly to an aspect and expressions that refer indirectly to an aspect by referring to some other property/entity that is related to the aspect. For example, Fine rooms is an explicitly expressed positive opinion concerning the Rooms aspect, while We had great views over the East River is an implicitly expressed positive opinion concerning the Location aspect, and All doors seem to have to slam to close is an implicit negative opinion concerning the Rooms aspect.
Each of the three annotators was assigned 139 unique reviews, plus 83 reviews which all three annotators independently annotated, for a total of 500 annotated reviews. The 83 shared reviews were used to measure inter-annotator agreement. To minimize bias in these measurements, the reviews were ordered in such a way that every eighth review was shared between all the annotators; this ensures that each annotator had the same amount of annotation experience when annotating the same shared review, and that these annotations are comparable to the remaining annotations in terms of annotator experience.During the annotation process, the annotators identified a number of corrupted reviews (primarily caused by site scraping errors in the original TripAdvisor data set).
These reviews were subsequently removed from the final dataset, which as a result consists of 442 reviews, of which 73 shared among all three annotators. The remaining 369 unique reviews were partitioned into a training set (258 reviews, 70% of the total) and a test set (111 reviews, 30% of the total). The data was split by selecting reviews for each subset in an interleaved fashion, so that each subset constitutes a minimally biased sample both with respect to the full dataset and with respect to annotator experience.You can download the TripAdvisor aspects annotated dataset here.
If you use the dataset please acknowledge its use with a citation: Marcheggiani, D., Täckström, O., Esuli, A, Sebastiani, F.: Hierarchical Multi-Label Conditional Random Fields for Aspect-Oriented Opinion Mining. In: Proceedings of the 36th European Conference on Information Retrieval (ECIR 2014).
1 Wang, H., Lu, Y., Zhai, C.: Latent aspect rating analysis on review text data: A rating regression approach. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2010). pp. 783–792. Washington, US (2010)