Sentiment Classification Using Automatically Extracted Subgraph Features

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  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 =          "",
  acmid =        "1860647",
  oai =          "oai:CiteSeerX.psu:",
  URL =          "",
  URL =          "",
  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

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