Summary of the paper

Title Bootstrapping Sentiment Labels For Unannotated Documents With Polarity PageRank
Authors Christian Scheible and Hinrich Schütze
Abstract We present a novel graph-theoretic method for the initial annotation of high-confidence training data for bootstrapping sentiment classifiers. We estimate polarity using topic-specific PageRank. Sentiment information is propagated from an initial seed lexicon through a joint graph representation of words and documents. We report improved classification accuracies across multiple domains for the base models and the maximum entropy model bootstrapped from the PageRank annotation.
Topics Document Classification, Text categorisation, Statistical and machine learning methods, Lexicon, lexical database
Full paper Bootstrapping Sentiment Labels For Unannotated Documents With Polarity PageRank
Bibtex @InProceedings{SCHEIBLE12.124,
  author = {Christian Scheible and Hinrich Schütze},
  title = {Bootstrapping Sentiment Labels For Unannotated Documents With Polarity PageRank},
  booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
  year = {2012},
  month = {may},
  date = {23-25},
  address = {Istanbul, Turkey},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-7-7},
  language = {english}
 }
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