An opinion about opinions about opinions: subjectivity and the aggregate reader

This opinion piece proposes that recent advances in opinion detection are limited in the extent to which they can detect important categories of opinion because they are not designed to capture some of the pragmatic aspects of opinion. A component of these is the perspective of the user of an opinion-mining system as to what an opinion really is, which is in itself a matter of opinion (metasubjectivity). We propose a way to define this component of opinion and describe the challenges it poses for corpus development and sentence-level detection technologies. Finally, we suggest that investment in techniques to handle metasubjectivity will likely bear costs but bring benefits in the longer term.

[1]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[2]  Claire Cardie,et al.  Joint Extraction of Entities and Relations for Opinion Recognition , 2006, EMNLP.

[3]  Iryna Gurevych,et al.  Extracting Opinion Targets in a Single and Cross-Domain Setting with Conditional Random Fields , 2010, EMNLP.

[4]  Christopher D. Manning,et al.  The Stanford Typed Dependencies Representation , 2008, CF+CDPE@COLING.

[5]  Vikas Sindhwani,et al.  Data Quality from Crowdsourcing: A Study of Annotation Selection Criteria , 2009, HLT-NAACL 2009.

[6]  Eduard Hovy,et al.  Extracting Opinions, Opinion Holders, and Topics Expressed in Online News Media Text , 2006 .

[7]  Christopher Potts,et al.  Did It Happen? The Pragmatic Complexity of Veridicality Assessment , 2012, CL.

[8]  Swapna Somasundaran,et al.  Recognizing Stances in Online Debates , 2009, ACL.

[9]  Brendan T. O'Connor,et al.  Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks , 2008, EMNLP.

[10]  Cecilia Ovesdotter Alm.  Subjective Natural Language Problems: Motivations, Applications, Characterizations, and Implications , 2011, ACL.

[11]  Kentaro Inui,et al.  Dependency Tree-based Sentiment Classification using CRFs with Hidden Variables , 2010, NAACL.

[12]  Amy Weinberg,et al.  Crowdsourcing syntactic relatedness judgements for opinion mining in the study of information technology adoption , 2011, LaTeCH@ACL.

[13]  Swapna Somasundaran,et al.  Finding the Sources and Targets of Subjective Expressions , 2008, LREC.

[14]  Pablo Gervás,et al.  A Joint Model of Feature Mining and Sentiment Analysis for Product Review Rating , 2011, ECIR.

[15]  Chun Chen,et al.  Opinion Word Expansion and Target Extraction through Double Propagation , 2011, CL.

[16]  Theresa Wilson.  Fine-grained subjectivity and sentiment analysis: recognizing the intensity, polarity, and attitudes of private states , 2008 .

[17]  Andrew McCallum,et al.  FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs , 2009, NIPS.