DICTIONARY EXTENSION FOR IMPROVING AUTOMATED SENTIMENT DETECTION

Johannes Liegl, Stefan Gindl, Arno Scharl, Alexander Hubmann-Haidvogel

Abstract

This paper investigates approaches to improve the accuracy of automated sentiment detection in textual knowledge repositories. Many high-throughput sentiment detection algorithms rely on sentiment dictionaries containing terms classified as either positive or negative. To obtain accurate and comprehensive sentiment dictionaries, we merge existing resources into a single dictionary and extend this dictionary by means of semisupervised learning algorithms such as Pointwise Mutual Information - Information Retrieval (PMI-IR) and Latent Semantic Analysis (LSA). The resulting extended dictionary is then evaluated on various datasets from different domains, which were annotated on both the document and sentence level.

References

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Paper Citation


in Harvard Style

Liegl J., Gindl S., Scharl A. and Hubmann-Haidvogel A. (2010). DICTIONARY EXTENSION FOR IMPROVING AUTOMATED SENTIMENT DETECTION . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010) ISBN 978-989-8425-28-7, pages 404-407. DOI: 10.5220/0003070304040407


in Bibtex Style

@conference{kdir10,
author={Johannes Liegl and Stefan Gindl and Arno Scharl and Alexander Hubmann-Haidvogel},
title={DICTIONARY EXTENSION FOR IMPROVING AUTOMATED SENTIMENT DETECTION},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)},
year={2010},
pages={404-407},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003070304040407},
isbn={978-989-8425-28-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)
TI - DICTIONARY EXTENSION FOR IMPROVING AUTOMATED SENTIMENT DETECTION
SN - 978-989-8425-28-7
AU - Liegl J.
AU - Gindl S.
AU - Scharl A.
AU - Hubmann-Haidvogel A.
PY - 2010
SP - 404
EP - 407
DO - 10.5220/0003070304040407