{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:52:33Z","timestamp":1760597553248},"reference-count":57,"publisher":"MIT Press - Journals","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Linguistics"],"published-print":{"date-parts":[[2013,9]]},"abstract":"<jats:p> Fine-grained opinion analysis methods often make use of linguistic features but typically do not take the interaction between opinions into account. This article describes a set of experiments that demonstrate that relational features, mainly derived from dependency-syntactic and semantic role structures, can significantly improve the performance of automatic systems for a number of fine-grained opinion analysis tasks: marking up opinion expressions, finding opinion holders, and determining the polarities of opinion expressions. These features make it possible to model the way opinions expressed in natural-language discourse interact in a sentence over arbitrary distances. The use of relations requires us to consider multiple opinions simultaneously, which makes the search for the optimal analysis intractable. However, a reranker can be used as a sufficiently accurate and efficient approximation. <\/jats:p><jats:p> A number of feature sets and machine learning approaches for the rerankers are evaluated. For the task of opinion expression extraction, the best model shows a 10-point absolute improvement in soft recall on the MPQA corpus over a conventional sequence labeler based on local contextual features, while precision decreases only slightly. Significant improvements are also seen for the extended tasks where holders and polarities are considered: 10 and 7 points in recall, respectively. In addition, the systems outperform previously published results for unlabeled (6 F-measure points) and polarity-labeled (10\u201315 points) opinion expression extraction. Finally, as an extrinsic evaluation, the extracted MPQA-style opinion expressions are used in practical opinion mining tasks. In all scenarios considered, the machine learning features derived from the opinion expressions lead to statistically significant improvements. <\/jats:p>","DOI":"10.1162\/coli_a_00141","type":"journal-article","created":{"date-parts":[[2012,11,16]],"date-time":"2012-11-16T14:50:51Z","timestamp":1353077451000},"page":"473-509","source":"Crossref","is-referenced-by-count":21,"title":["Relational Features in Fine-Grained Opinion Analysis"],"prefix":"10.1162","volume":"39","author":[{"given":"Richard","family":"Johansson","sequence":"first","affiliation":[{"name":"University of Gothenburg"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alessandro","family":"Moschitti","sequence":"additional","affiliation":[{"name":"University of Trento"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","reference":[{"key":"R1","doi-asserted-by":"publisher","DOI":"10.1075\/li.32.2.10ash"},{"key":"R2","first-page":"125","volume-title":"Computing Attitude and Affect in Text: Theory and Applications.","author":"Bethard Steven","year":"2005"},{"key":"R3","first-page":"440","volume-title":"Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL-07)","author":"Blitzer John","year":"2007"},{"key":"R4","doi-asserted-by":"publisher","DOI":"10.1145\/130385.130401"},{"key":"R5","first-page":"2,683","volume-title":"IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence","author":"Breck Eric","year":"2007"},{"key":"R6","doi-asserted-by":"publisher","DOI":"10.3115\/1610075.1610136"},{"key":"R7","doi-asserted-by":"publisher","DOI":"10.3115\/1613715.1613816"},{"key":"R8","first-page":"269","volume-title":"Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics","author":"Choi Yejin","year":"2010"},{"key":"R9","first-page":"175","volume-title":"Proceedings of the Seventeenth International Conference on Machine Learning","author":"Collins Michael","year":"2000"},{"key":"R10","doi-asserted-by":"publisher","DOI":"10.3115\/1118693.1118694"},{"issue":"7","key":"R11","first-page":"551","volume":"2006","author":"Crammer Koby","year":"2006","journal-title":"Journal of Machine Learning Research"},{"issue":"2","key":"R12","first-page":"265","volume":"2001","author":"Crammer Koby","year":"2001","journal-title":"Journal of Machine Learning Research"},{"key":"R13","first-page":"1871","volume":"9","author":"Fan Rong-En","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"R14","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007662407062"},{"key":"R15","first-page":"1,583","volume-title":"Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics","author":"Gerber Matthew","year":"2010"},{"key":"R16","doi-asserted-by":"publisher","DOI":"10.3115\/1620754.1620827"},{"key":"R17","volume-title":"Computer Intensive Statistical Methods.","author":"Hjorth J. 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