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

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on which corpus they tested against.
Neviarouskaya et al. [126] developed a system for computing the sentiment
of a sentence <strong>based</strong> on the words in the sentence, using Martin and White’s [110]
appraisal theory and Izard’s [74] affect categories. They used a complicated set of
rules for composing attitudes found in different places in a sentence to come up with
an overall label for the sentence. They achieved 62.1% accuracy at determining the
fine-grained attitude types of each sentence in their corpus, and 87.9% accuracy at
categorizing sentences as positive, negative, or neutral.
2.5 Structural sentiment extraction techniques
After demonstrating techniques for classifying full reviews or individual sentences
with high accuracy, work in sentiment analysis turned toward deeper extraction
methods, focused on determining parts of the sentiment structure, such as what
a sentiment is about (the target), and who is expressing it (the source). Numerous researchers
have performed work in this area, and there have been many different ways
of evaluating structured sentiment analysis techniques. Table 2.1 highlights results
reported by the some of the papers discussed in this section.
Among the techniques that focus specifically on evaluation, Nigam and Hurst
[128] use part-of-speech extraction patterns and a manually-constructed sentiment
lexicon to identify positive and negative phrases. They use a sentence-level classifier
to determine whether each sentence of the document is relevant to a given topic,
and assign all of the extracted sentiment phrases to that topic. They further discuss
methods of assigning a sentiment score for a particular topic using the results of their
system.
Most of the other techniques that have been developed for opinion extraction
have focused on product reviews, and on finding product features and the opinions