Making objective decisions from subjective data: Detecting irony in customers reviews

[EN] The research described in this work focuses on identifying key components for the task of irony detection. By means of analyzing a set of customer reviews, which are considered ironic both in social and mass media, we try to find hints about how to deal with this task from a computational point of view. Our objective is to gather a set of discriminating elements to represent irony, in particular, the kind of irony expressed in such reviews. To this end, we built a freely available data set with ironic reviews collected from Amazon. Such reviews were posted on the basis of an online viral effect; i.e. contents that trigger a chain reaction in people. The findings were assessed employing three classifiers. Initial results are largely positive, and provide valuable insights into the subjective issues of language facing tasks such as sentiment analysis, opinion mining and decision making. (C) 2012 Elsevier B.V. All rights reserved.The National Council for Science and Technology (CONACyT - Mexico) has funded the research of the first author. The European Commission as part of the WIQEI IRSES-Project (grant no. 269180) within the FP 7 Marie Curie People Framework has partially funded this work. This work was carried out in the framework of the MICINN Text-Enterprise (TIN2009-13391-C04-03) research project and the Microcluster VLC/Campus (International Campus of Excellence) on Multimodal Intelligent Systems.Reyes Pérez, A.; Rosso, P. (2012). Making objective decisions from subjective data: Detecting irony in customers reviews. Decision Support Systems. 53(4):754-760. https://doi.org/10.1016/j.dss.2012.05.027S75476053

Statistical Substring Reduction in Linear Time

We study the problem of efficiently removing equal frequency n-gram substrings from an n-gram set, formally called Statistical Substring Reduction (SSR). SSR is a useful operation in corpus based multi-word unit research and new word identification task of oriental language processing. We present a new SSR algorithm that has linear time (O(n)), and prove its equivalence with the traditional O(n) algorithm