Structured Models for Fine-to-Coarse Sentiment Analysis

In this paper we investigate a structured model for jointly classifying the sentiment of text at varying levels of granularity. Inference in the model is based on standard sequence classification techniques using constrained Viterbi to ensure consistent solutions. The primary advantage of such a model is that it allows classification decisions from one level in the text to influence decisions at another. Experiments show that this method can significantly reduce classification error relative to models trained in isolation.

[1]  Matt Thomas,et al.  Get out the vote: Determining support or opposition from Congressional floor-debate transcripts , 2006, EMNLP.

[2]  Andrew McCallum,et al.  Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data , 2004, J. Mach. Learn. Res..

[3]  Ben Taskar,et al.  Max-Margin Parsing , 2004, EMNLP.

[4]  Bo Pang,et al.  A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.

[5]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[6]  Andrew McCallum,et al.  An Introduction to Conditional Random Fields for Relational Learning , 2007 .

[7]  Thomas Hofmann,et al.  Support vector machine learning for interdependent and structured output spaces , 2004, ICML.

[8]  Oren Etzioni,et al.  Extracting Product Features and Opinions from Reviews , 2005, HLT.

[9]  John Langford,et al.  Search-based structured prediction , 2009, Machine Learning.

[10]  Michael Collins,et al.  Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms , 2002, EMNLP.

[11]  Claire Cardie,et al.  Identifying Sources of Opinions with Conditional Random Fields and Extraction Patterns , 2005, HLT.

[12]  Ben Taskar,et al.  Max-Margin Markov Networks , 2003, NIPS.

[13]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[14]  Koby Crammer,et al.  Ultraconservative Online Algorithms for Multiclass Problems , 2001, J. Mach. Learn. Res..

[15]  Dan Roth,et al.  A Linear Programming Formulation for Global Inference in Natural Language Tasks , 2004, CoNLL.

[16]  Peter D. Turney Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[17]  Trevor Darrell,et al.  Conditional Random Fields for Object Recognition , 2004, NIPS.

[18]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

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

[20]  Koby Crammer,et al.  Online Large-Margin Training of Dependency Parsers , 2005, ACL.

[21]  Scott Miller,et al.  A Novel Use of Statistical Parsing to Extract Information from Text , 2000, ANLP.

[22]  Fernando Pereira,et al.  Discriminative learning and spanning tree algorithms for dependency parsing , 2006 .

[23]  Yi Mao,et al.  Isotonic Conditional Random Fields and Local Sentiment Flow , 2006, NIPS.

[24]  Ben Taskar,et al.  An End-to-End Discriminative Approach to Machine Translation , 2006, ACL.