Structured Models for Fine-to-Coarse Sentiment Analysis
This Paper is reviewed for Social Media Analysis 10-802 in Fall 2012.
Citation
Ryan Mcdonald , Kerry Hannan , Tyler Neylon , Mike Wells , Jeff Reynar, 2007, In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics .
Online version
Structured Models for Fine-to-Coarse Sentiment Analysis
Summary
This paper proposes a novel approach to finding sentiments at several granular levels (document, paragraph, sentence, phrase, word ). This paper introduces a single structured model that transforms the multi-level sentiment classification task into a a single problem of learning from sequence of granular components using constrained viterbi. Single model approach performs better than models trained in isolation for a given level of granularity. It considers two important ideas in modelling, firstly, higher level classification can benefit from granular level classification and secondly, granular level classification can benefit from higher level classification.
Results
The results are given for two levels of granularity (sentence and document). Three baseline systems for comparison
- Document-classifier learns to predict document label only.
- Sentence-classifier learns to predict isolated sentence label only.
- Sentence-Structured learns to predict the sentence label by considering the document as a sequence of sentences and uses sequence chain model.
Two alternative to fine-to-coarse systems ( cascaded models ) for comparison
- Sentence-Structured Model->Document Classifier
- Document Classifier->Sentence-Structured Model
The proposed model is called Joint-Structured.
Discussion
Related papers
- Choi et al 2005, Choi et al 2006
- Uses CRFs to learn global sequence model to classify and assign sources to opinion.
- Mao and G. Lebanon, 2006
- Uses sequential CRF regression model to measure polarity on the sentence level in order to determine sentiment flow of authors in reviews.
- Pang and Lee, 2004
- Cascaded Model ( alternative to fine-to-coarse methodology)
