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

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at finding entity-opinion pairs (depending which set of camera reviews they use to
evaluate).
Li et al. [99] describe a technique for finding attitudes and product features
using CRFs of various topologies. They then pair them by taking the closest opinion
word for each product feature.
Jakob and Gurevych [75] extract opinion target mentions in their corpus of
service reviews [77] using a linear CRF. Their corpus is publicly available and its
advantages and flaws are discussed in Section 5.3.
Kessler and Nicolov [87] performed an experiment in which they had human
taggers identify “sentiment expressions” as well as “mentions” covering all of the
important product features in a particular domain, whether or not those mentions
were the target of a sentiment expression, and had their taggers identify which of
those mentions were opinion targets. They used SVM ranking to determine, from
among the available mentions, which mention was the target of each opinion. Their
corpus is publicly available and its advantages and flaws are discussed in Section 5.4.
Cruz et al. [40] complain that the idea of learning product features from a
collection of reviews about a single product is too domain independent, and propose
to make the task even more domain specific by using interactive methods to introduce
a product-feature hierarchy, domain specific lexicon, and learning other resources from
an annotated corpus.
Lakkaraju et al. [95] describe a graphical model for finding sentiments and the
“facets” of a product described in reviews. The compare three models with different
levels of complexity.
FACTS is a sequence model, where each word is generated
by 3 variables: a facet variable, a sentiment variable, and a selector variable (which
determines whether to draw the word <strong>based</strong> on facet, sentiment, or as a non-sentiment