Sentiment Composition Using a Parabolic Model

In this paper, we propose a computational model that accounts for the effects of negation and modality on opinion expressions. Based on linguistic experiments informed by native speakers, we distil these effects according to the type of modality and negation. The model relies on a parabolic representation where an opinion expression is represented as a point on a parabola. Negation is modelled as functions over this parabola whereas modality through a family of parabolas of different slopes; each slope corresponds to a different certainty degree. The model is evaluated using two experiments, one involving direct strength judgements on a 7-point scale and the other relying on a sentiment annotated corpus. The empirical evaluation of our model shows that it matches the way humans handle negation and modality in opinionated sentences

[1]  Yann Mathet,et al.  Classification de textes d'opinions : une approche mixte n-grammes et sémantique , 2007 .

[2]  Annie Zaenen,et al.  Contextual Valence Shifters , 2006, Computing Attitude and Affect in Text.

[3]  Claire Cardie,et al.  Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis , 2008, EMNLP.

[4]  Mitsuru Ishizuka,et al.  Assessing Sentiment of Text by Semantic Dependency and Contextual Valence Analysis , 2007, ACII.

[5]  Naoaki Okazaki,et al.  Opinion classification with tree kernel SVM using linguistic modality analysis , 2009, CIKM.

[6]  Jeffrey Pennington,et al.  Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions , 2011, EMNLP.

[7]  Karo Moilanen,et al.  Sentiment Composition , 2007 .

[8]  Claire Cardie,et al.  Compositional Matrix-Space Models for Sentiment Analysis , 2011, EMNLP.

[9]  Nicholas Asher,et al.  How do Negation and Modality Impact on Opinions? , 2012, ExProM@ACL.

[10]  David R. Dowty,et al.  Introduction to Montague semantics , 1980 .

[11]  Anne Abeillé,et al.  The Grande Grammaire du Français Project , 2010, LREC.

[12]  Andrew Y. Ng,et al.  Semantic Compositionality through Recursive Matrix-Vector Spaces , 2012, EMNLP.

[13]  Christopher D. Manning,et al.  Finding Contradictions in Text , 2008, ACL.

[14]  Paul Larreya,et al.  L'expression de la modalité en français et en anglais (domaine verbal) , 2004 .

[15]  Xuanjing Huang,et al.  Structural Opinion Mining for Graph-based Sentiment Representation , 2011, EMNLP.

[16]  Clement T. Yu,et al.  The effect of negation on sentiment analysis and retrieval effectiveness , 2009, CIKM.

[17]  Dietrich Klakow,et al.  A survey on the role of negation in sentiment analysis , 2010, NeSp-NLP@ACL.

[18]  James Pustejovsky,et al.  FactBank: a corpus annotated with event factuality , 2009, Lang. Resour. Evaluation.

[19]  János Csirik,et al.  The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes , 2008, BMC Bioinformatics.

[20]  György Szarvas,et al.  Hedge Classification in Biomedical Texts with a Weakly Supervised Selection of Keywords , 2008, ACL.

[21]  Maite Taboada,et al.  Lexicon-Based Methods for Sentiment Analysis , 2011, CL.