Difference between revisions of "Structured Models for Fine-to-Coarse Sentiment Analysis"
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== Summary == | == Summary == | ||
| − | This paper proposes a novel approach to | + | This paper proposes a novel approach to finding sentiments at several granular levels (document, paragraph, sentence, phrase, word ). This paper introduces a single [[UsesMethod::structured model]] that transforms the multi-level sentiment classification task into a a single problem of learning from sequence of granular components using constrained [[UsesMethod::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 == | == Results == | ||
Revision as of 21:01, 5 November 2012
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.
