Sentiment Analysis based on Appraisal Theory and Functional Local ...

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probabilistic parsing [33], machine translation [150], and question answering [141]. In
this way, FLAG adheres to the principle of least commitment [107, 118, 162], putting
off decisions about which patterns are correct until it has as much information as
possible about the text each pattern identifies.
H1: The three step process of finding attitude groups, identifying the potential appraisal
expression structures for each attitude group, and then selecting the best
one can accurately extract targets in domains such as blogs, where one can’t
take advantage of redundancy to create or use domain-specific resources as part
of the appraisal extraction process.
The first step in FLAG’s operation is to detect ranges of text which are candidates
for parsing. This is done by finding opinion phrases which are constructed from
opinion head words and modifiers listed in a lexicon. The lexicon lists positive and
negative opinion words and modifiers with the options they realize in the Attitude
system. This lexicon is used to locate opinion phrases, possibly generating multiple
possible interpretations of the same phrase.
The second step in FLAG’s extraction process is to determine a set of potential
appraisal expression instances for each attitude group, using a set of linkage specifications
(patterns in a dependency parse of the sentence that represent patterns in
the local grammar of evaluation) to identify the targets, evaluators, and other parts
of each potential appraisal expression instance. Using these linkage specifications,
FLAG is expected, in general, to find several patterns for each attitude found in the
first step.
It is time consuming to develop a list of patterns, and a relatively unintuitive
task for any developer who would have to develop this list. Therefore, I have developed
a supervised learning technique that can learn these local grammar patterns from an