Istituto di Scienza e Tecnologie dell'Informazione     
Marcheggiani D., Tackstrom O., Esuli A., Sebastiani F. Hierarchical multi-label conditional random fields for aspect-oriented opinion mining. In: ECIR 2014 - Advances in Information Retrieval. 36th European Conference on Information Retrieval (Amsterdam, The Netherlands, 13-16 April 2014). Proceedings, pp. 273 - 285. Maarten de Rijke, Tom Kenter, Arjen P. de Vries, ChengXiang Zhai, Franciska de Jong, Kira Radinsky, Katja Hofmann (eds.). (Lecture Notes in Computer Science, vol. 8416). Springer Verlag, 2014.
A common feature of many online review sites is the use of an overall rating that summarizes the opinions expressed in a review. Unfortunately, these document-level ratings do not provide any information about the opinions contained in the review that concern a specific aspect (e.g., cleanliness) of the product being reviewed (e.g., a hotel). In this paper we study the finer-grained problem of aspect-oriented opinion mining at the sentence level, which consists of predicting, for all sentences in the review, whether the sentence expresses a positive, neutral, or negative opinion (or no opinion at all) about a specific aspect of the product. For this task we propose a set of increasingly powerful models based on conditional random fields (CRFs), including a hierarchical multi-label CRFs scheme that jointly models the overall opinion expressed in the review and the set of aspect-specific opinions expressed in each of its sentences. We evaluate the proposed models against a dataset of hotel reviews (which we here make publicly available) in which the set of aspects and the opinions expressed concerning them are manually annotated at the sentence level. We find that both hierarchical and multi-label factors lead to improved predictions of aspect-oriented opinions.
URL: http://link.springer.com/chapter/10.1007%2F978-3-319-06028-6_23
DOI: 10.1007/978-3-319-06028-6_23
Subject Topic models
Probabilistic Graphical Models
I.2.6 Learning

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