{"word_count": 208, "character_count": 1495}
Our study is based on a dataset of online restaurant reviews in French. We have chosen Michelin-starred restaurants and non-starred restaurants, respectively 63 restaurants in Paris, from review sites. The total dataset, or corpus of reviews, consists of 1,000 reviews written in French from 126 restaurants. To produce annotated data, we first segmented 1,012 reviews into sentences. Then three annotators manually annotated each sentence into one of four evaluation types (which will be described in detail future posts):(1) the reviewer’s opinion on the restaurant (positive, negative, or mixed opinion);(2) the reviewer’s input/feedback to potential customers and restaurant owners (suggestion, advice, or warning)(3) whether the reviewer wants to return to the restaurant (intention); and(4) the reviewer’s neutral statement about the experience (description).Using Fleiss’s Kappa measure, we obtained 0.90, which is considered ‘almost perfect’, according to the Landis and Koch (1977) scale. As a result, we obtained 2,943 annotated sentences, yet the class distribution is strongly unbalanced. Our approaches for solving such class imbalance problems will also be described in future posts.ReferencesLandis, J. Richard. and Gary G. Koch. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159-174.Cite this article as: Hyun Jung KANG, "The dataset: online restaurant reviews in French", in The Language of X, 26/02/2019, https://langofx.hypotheses.org/78.