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.
Most recently, REF investigate a structured model for jointly classifying the sentiment of text at varying levels of granularity.
Structured Models for Fine-to-Coarse Sentiment Analysis
45th Annual Meeting of the Association of Computational Linguistics
Computer Science