Supervised Opinion Frames Detection with RAID

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 548)


Most systems for opinion analysis focus on the classification of opinion polarities and rarely consider the task of identifying the different elements and relations forming an opinion frame. In this paper, we present RAID, a tool featuring a processing pipeline for the extraction of opinion frames from text with their opinion expressions, holders, targets and polarities. RAID leverages a lexical, syntactic and semantic analysis of text, using several NLP tools such as dependency parsing, semantic role labelling, named entity recognition and word sense disambiguation. In addition, linguistic resources such as SenticNet and the MPQA Subjectivity Lexicon are used both to locate opinions in the text and to classify their polarities according to a fuzzy model that combines the sentiment values of different opinion words. RAID was evaluated on three different datasets and is released as open source software under the GPLv3 license.


Natural Language Processing Fuzzy Membership Function Conditional Random Field Word Sense Disambiguation Opinion Extraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Fondazione Bruno KesslerTrentoItaly

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