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.

• Type: Conference
• Year: 2007
• Where: ACL
• Authors: Ryan T. McDonald, Kerry Hannan, Tyler Neylon, Mike Wells, Jeffrey C. Reynar
• Keywords: ACL 2007, Computational Linguistics, Constrained Viterbi, Standard Sequence Classification, Structured Model