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} } |