Paper: Structured Models for Fine-to-Coarse Sentiment Analysis

ACL ID P07-1055
Title Structured Models for Fine-to-Coarse Sentiment Analysis
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007
Authors

In this paper we investigate a structured model for jointly classifying the sentiment of text at varying levels of granularity. Infer- ence in the model is based on standard se- quence classification techniques using con- strained Viterbi to ensure consistent solu- tions. The primary advantage of such a model is that it allows classification deci- sions from one level in the text to influence decisions at another. Experiments show that this method can significantly reduce classifi- cation error relative to models trained in iso- lation.