Structured Models for Fine-to-Coarse Sentiment Analysis

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Structured Models for Fine-to-Coarse Sentiment Analysis (2007)

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by Ryan Mcdonald , Kerry Hannan , Tyler Neylon , Mike Wells , Jeff Reynar

Venue:	Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics
Citations:	89 - 6 self

BibTeX

@INPROCEEDINGS{Mcdonald07structuredmodels,
    author = {Ryan Mcdonald and Kerry Hannan and Tyler Neylon and Mike Wells and Jeff Reynar},
    title = {Structured Models for Fine-to-Coarse Sentiment Analysis},
    booktitle = {Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics},
    year = {2007}
}

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Abstract

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

Keyphrases

structured model fine-to-coarse sentiment analysis primary advantage consistent solution classification decision classification error relative standard sequence classification technique

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