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

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find opinion words that describe the product features that have already been found.
The customers of sentiment analysis applications are interested in mining a
broader range of texts such as blogs, chat rooms, message boards, and social networking
sites [10, 98]. They’re interested in finding favorable and unfavorable comparisons
of their product in reviews of other products. They’re interested in mining perceptions
of a their brand, just as much as they’re interested in mining perceptions of
a their company’s products.
For these reasons, sentiment analysis needs to move
beyond the assumption that all texts of interest are review-like.
The assumption that the important opinions in a document are evaluations of
product features breaks down completely when performing sentiment analysis on blog
posts or tweets. In these domains, it may be difficult to curate a large collection of
text on a single narrowly-defined topic, or the users of a sentiment analysis technique
may not be interested in operating on only a single narrowly-defined topic. O’Hare
et al. [131], for example, observed that in the domain of financial blogs, 30% of the
documents encountered are relevant to at least one stock, but each of those documents
is relevant to three different stocks on average. This would make the assumption of
lexical redundancy for opinion targets unsupportable.
To enable a fine-grained evaluation of appraisal expression extraction systems
in these more general sentiment analysis domains, I have created the IIT <strong>Sentiment</strong>
Corpus, a corpus of blog posts annotated with all of the appraisal expressions that
were there to be found, regardless of topic.
1.4 FLAG: Functional Local Appraisal Grammar Extractor
To move beyond the review-centric view of appraisal extraction that others
in sentiment analysis research have been working with, I have developed FLAG, an
appraisal expression extractor that doesn’t rely on domain-dependent features to find