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

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CHAPTER 2
PRIOR WORK
This chapter gives a general background on applications and techniques that
have been used to study evaluation for sentiment analysis, particularly those related
to extracting individual evaluations from text. A comprehensive view of the field of
sentiment analysis is given in a survey article by Pang and Lee [133]. This chapter
also discusses local grammar techniques and information extraction techniques that
are relevant to extracting individual evaluations from text.
2.1 Applications of <strong>Sentiment</strong> <strong>Analysis</strong>
<strong>Sentiment</strong> analysis has a number of interesting applications [133]. It can be
used in recommendation systems (to recommend only products that consumers liked)
[165], ad-placement applications (to avoid advertising a company alongside an article
that is bad press for them) [79], and flame detection systems (to identify and remove
message board postings that contain antagonistic language) [157].
It can also be
used as a component technology in topical information retrieval systems (to discard
subjective sections of documents and improve retrieval accuracy).
Structured extraction of evaluative language in particular can be used for
multiple-viewpoint summarization, summarizing reviews and other social media for
business intelligence [10, 98], for predicting product demand [120] or product pricing
[5], and for political analysis.
One example of a higher-level task that depends on structured sentiment extraction
is Archak et al.’s [5] technique for modeling the pricing effect of consumer
opinion on products. They posit that demand for a product is driven by the price of
the product and consumer opinion about the product. They model consumer opinion
about a product by constructing, for each review, a matrix with product features