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

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text, their data for sentence-level tasks is derived from the MPQA corpus [177, 179]
(which annotates sub-sentence spans of subjective text), and considers a sentence
subjective if the sentence has any subjective spans of sufficient strength within it.
Thus, their sentence-level data derives its validity from the fact that it’s derived from
the corpus’s finer-grained subjectivity annotations that they suppose an automated
system would be interested in using or discarding.
Hurst and Nigam [73] write that recognizing sentences as having positive or
negative polarity derives its validity from the goal of “[identifying] sentences that
could be efficiently scanned by a marketing analyst to identify salient quotes to use
in support of positive or negative marketing conclusions.” [128, describing 73] They
too perform sentiment extraction at a phrase level.
In the works described above, the authors behind each task have a specific
justification for why sentence level sentiment analysis is valid, and the way in which
they derive their sentence-level annotations from finer-grained annotations and the
way in they approach the sentiment analysis task reflects the justification they give
for the validity of sentence-level sentiment analysis. But somewhere int he development
of the sentence-level sentiment analysis task, researchers lost their focus on the
rather limited justifications of sentence-level sentiment analysis that I have discussed,
and began to assume that whole sentences intrinsically reflect a single sentiment at
a time or a single overall sentiment. (I do not understand why this assumption is
valid, and I have yet to find a convincing justification in the literature.) In work that
operates from this assumption, sentence-level sentiment annotations are not derived
from finer-grained sentiment annotations. Instead, the sentence-level sentiment annotations
are assigned directly by human annotators. For example, Jakob et al. [77]
developed a corpus of finer-grained sentiment annotations by first having their annotators
determine which sentences were topic-relevant and opinionated, working to