Sentiment Classification Using Automatically Extracted Subgraph Features

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Arora:2010:NAACL,

• author = "Shilpa Arora and Elijah Mayfield and Carolyn Penstein-Rose and Eric Nyberg",
• title = "Sentiment Classification Using Automatically Extracted Subgraph Features",
• booktitle = "Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text",
• series = "CAAGET '10",
• year = "2010",
• address = "Los Angeles, California",
• pages = "131--139",
• month = jun,
• keywords = "genetic algorithms, genetic programming, GP",
• URL = "http://dl.acm.org/citation.cfm?id=1860631.1860647",
• acmid = "1860647",
• oai = "oai:CiteSeerX.psu:10.1.1.207.7440",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.207.7440",
• URL = "http://www.cs.cmu.edu/%7Eemayfiel/AroraMayfieldRoseNybergNAACL2010.pdf",
• publisher = "Association for Computational Linguistics",
• publisher_address = "Stroudsburg, PA, USA",
• size = "9 pages",
• abstract = "In this work, we propose a novel representation of text based on patterns derived from linguistic annotation graphs. We use a subgraph mining algorithm to automatically derive features as frequent subgraphs from the annotation graph. This process generates a very large number of features, many of which are highly correlated. We propose a genetic programming based approach to feature construction which creates a fixed number of strong classification predictors from these subgraphs. We evaluate the benefit gained from evolved structured features, when used in addition to the bag-of-words features, for a sentiment classification task.",

}

Genetic Programming entries for Shilpa Arora Elijah Mayfield Carolyn Penstein Rose Eric Nyberg

Citations