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

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Affect [2, 3, 20, 110, 156] concerns the emotions that people feel, whether in
response to a trigger or not, whether positive, negative, or neither (e.g. surprise).
Affect and evaluation have a lot of overlap, in that positive and negative emotions
triggered by a particular trigger often constitute an evaluation of that trigger [110].
Because of this, affect is always included in studies of evaluation, and particular
frameworks for classifying different types of affect (e.g. appraisal theory [110]) are
particularly well suited for evaluation tasks. Affect can also have applications outside
of evaluation, in fields like human-computer interaction [3, 189, 190], and also in
applications outside of text analysis. Alm [3], for example, focused on identifying
spans of text in stories which conveyed particular emotions, so that a computerized
storyteller could vocalize those sections of a story with appropriately dramatic voices.
Her framework for dealing with affect involved identifying the emotions “angry”,
“disgusted”, “fearful”, “happy”, “sad”, and “surprised.” These emotion types are
motivated (appropriately for the task) by the fact that they should be vocalized
differently from each other, but because this framework lacks a unified concept of
positive and negative emotions, it would not be appropriate for studying evaluative
language.
There are many other non-objective aspects of texts that are interesting for
different applications in the field of sentiment analysis, and a blanket term for these
non-objective aspects of texts is “subjectivity”. The most general studies of subjectivity
have focused on how “private states”, internal states that can’t be observed
directly by others, are expressed [174, 179].
More specific aspects of subjectivity
include predictive opinions [90], speculation about what will happen in the future,
recommendations of a course of action, and the intensity of rhetoric [158]. <strong>Sentiment</strong>
analysis whose goal is to classify text for intensity of rhetoric, for example, can be
used to identify flames (postings that contain antagonistic language) on a message
board for moderator attention.