Sentiment detection automatically identifies emotions in textual data.
The increasing amount of emotive documents available in corporate
databases and on the World Wide Web calls for automated methods to
process this important source of knowledge. Sentiment detection draws
attention from researchers and practitioners alike - to enrich business
intelligence applications, for example, or to measure the impact
of customer reviews on purchasing decisions. Most sentiment detection
approaches do not consider language ambiguity, despite the fact that
one and the same sentiment term might differ in polarity depending
on the context, in which a statement is made. To address this shortcoming,
this paper introduces a novel method that uses Naïve Bayes to identify
ambiguous terms. A contextualized sentiment lexicon stores the polarity
of these terms, together with a set of co-occurring context terms.
A formal evaluation of the assigned polarities confirms that considering
the usage context of ambiguous terms improves the accuracy of high-throughput
sentiment detection methods. Such methods are a prerequisite for
using sentiment as a metadata element in storage and distributed
file-level intelligence applications, as well as in enterprise portals
that provide a semantic repository of an organization's information
assets.
%0 Journal Article
%1 weichselbraun2010e
%A Weichselbraun, Albert
%A Gindl, Stefan
%A Scharl, Arno
%D 2010
%J Journal of Information and Data Management
%K annotation, document enrichment, language learning, machine natural processing
%N 3
%P 329--342
%T A Context-Dependent Supervised Learning Approach to Sentiment Detection
in Large Textual Databases
%V 1
%X Sentiment detection automatically identifies emotions in textual data.
The increasing amount of emotive documents available in corporate
databases and on the World Wide Web calls for automated methods to
process this important source of knowledge. Sentiment detection draws
attention from researchers and practitioners alike - to enrich business
intelligence applications, for example, or to measure the impact
of customer reviews on purchasing decisions. Most sentiment detection
approaches do not consider language ambiguity, despite the fact that
one and the same sentiment term might differ in polarity depending
on the context, in which a statement is made. To address this shortcoming,
this paper introduces a novel method that uses Naïve Bayes to identify
ambiguous terms. A contextualized sentiment lexicon stores the polarity
of these terms, together with a set of co-occurring context terms.
A formal evaluation of the assigned polarities confirms that considering
the usage context of ambiguous terms improves the accuracy of high-throughput
sentiment detection methods. Such methods are a prerequisite for
using sentiment as a metadata element in storage and distributed
file-level intelligence applications, as well as in enterprise portals
that provide a semantic repository of an organization's information
assets.
@article{weichselbraun2010e,
abstract = {Sentiment detection automatically identifies emotions in textual data.
The increasing amount of emotive documents available in corporate
databases and on the World Wide Web calls for automated methods to
process this important source of knowledge. Sentiment detection draws
attention from researchers and practitioners alike - to enrich business
intelligence applications, for example, or to measure the impact
of customer reviews on purchasing decisions. Most sentiment detection
approaches do not consider language ambiguity, despite the fact that
one and the same sentiment term might differ in polarity depending
on the context, in which a statement is made. To address this shortcoming,
this paper introduces a novel method that uses Naïve Bayes to identify
ambiguous terms. A contextualized sentiment lexicon stores the polarity
of these terms, together with a set of co-occurring context terms.
A formal evaluation of the assigned polarities confirms that considering
the usage context of ambiguous terms improves the accuracy of high-throughput
sentiment detection methods. Such methods are a prerequisite for
using sentiment as a metadata element in storage and distributed
file-level intelligence applications, as well as in enterprise portals
that provide a semantic repository of an organization's information
assets.},
added-at = {2012-04-16T19:17:24.000+0200},
author = {Weichselbraun, Albert and Gindl, Stefan and Scharl, Arno},
biburl = {https://www.bibsonomy.org/bibtex/26bd33c79a8fac3bf6ea0d32cbda29c8c/albert.weichselbraun},
eprint = {http://eprints.weblyzard.com/25/1/jidm%2Dpublished%2D1.pdf},
interhash = {1b743adfbf7440f702ce4a7ade6f50c3},
intrahash = {6bd33c79a8fac3bf6ea0d32cbda29c8c},
journal = {Journal of Information and Data Management},
keywords = {annotation, document enrichment, language learning, machine natural processing},
number = 3,
pages = {329--342},
timestamp = {2012-04-16T19:17:27.000+0200},
title = {A Context-Dependent Supervised Learning Approach to Sentiment Detection
in Large Textual Databases},
volume = 1,
year = 2010
}