The main focus of this paper is to investigate methods for opinion extraction at a more detailed level of granularity, retrieving not only the opinionated portion of text, but also the target of that expressed opinion. We describe a novel approach to fine-grained opinion mining that, after an initial lexicon based processing step, treats the problem of finding the opinion expressed towards an entity as a relation classification task. We detail a classification workflow that combines the initial lexicon based module with a broader classification part that involves two different models, one for relation classification and the other for sentiment polarity shift identification. We provided detailed descriptions of a series of classification experiments in which we use an original proximity based bag-of-words model. We also introduce a new use of syntactic features used together with a tree kernel for both the relation and sentiment polarity shift classification tasks.
@InProceedings{ginsca:OASIcs.ICCSW.2012.56, author = {Ginsca, Alexandru Lucian}, title = {{Fine-Grained Opinion Mining as a Relation Classification Problem}}, booktitle = {2012 Imperial College Computing Student Workshop}, pages = {56--61}, series = {Open Access Series in Informatics (OASIcs)}, ISBN = {978-3-939897-48-4}, ISSN = {2190-6807}, year = {2012}, volume = {28}, editor = {Jones, Andrew V.}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2012.56}, URN = {urn:nbn:de:0030-drops-37653}, doi = {10.4230/OASIcs.ICCSW.2012.56}, annote = {Keywords: Opinion Mining, Opinion Target Identification, Syntactic Features} }
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