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

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for studying subjectivity. The MPQA corpus is publicly available and it advantages
and flaws are discussed in Section 5.1. They have not described an integrated system
for sentiment extraction, and many of the experiments that they have performed have
involved automatically boiling down the ground truth annotations into something
more tractable for a computer to match. They’ve generally avoided trying to extract
spans of text, preferring to take the existing ground truth annotations and classify
them.
2.6 Opinion lexicon construction
Lexicon-<strong>based</strong> approaches to sentiment analysis often require large hand-built
lexicons to identify opinion words. These lexicons can be time-consuming to construct,
so there has been a lot of research into techniques for automatically building
lexicons of positive and negative words.
Hatzivassiloglou and McKeown [66] developed a graph-<strong>based</strong> technique for
learning lexicons by reading a corpus. In their technique, they find pairs of adjectives
conjoined by conjunctions (e.g. “fair and legitimate” or “fair but brutal”), as well as
morphologically related adjectives (e.g. “thoughtful” and “thoughtless”), and create
a graph where the vertices represent words, and the edges represent pairs (marked
as same-orientation or opposite-orientation links).
They apply a graph clustering
algorithm to cluster the adjectives found into two clusters of positive and negative
terms. This technique achieved 82% accuracy at classifying the words found.
Another algorithm for constructing lexicons is that of Turney and Littman
[171]. They determine whether words are positive or negative and how strong the
evaluation is by computing the words’ pointwise mutual information (PMI) for their
co-occurrence with small set of positive seed words and a small set of negative seed
words. Unlike their earlier work [170], which I mentioned in Section 2.3, the seed