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

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1.1 <strong>Sentiment</strong> Classification versus <strong>Sentiment</strong> Extraction
To understand the additional information that can be obtained by identifying
structured representations of opinions, consider an example of a classification task,
typical of the kinds of opinion summarization applications performed today — movie
review classification. In movie review classification, the goal is to determine whether
the reviewer liked the movie <strong>based</strong> on the text of the review. This task was a popular
starting point for sentiment analysis research, since it was easy to construct corpora
from product review websites and movie review websites by turning the number of
stars on the review into class labels indicating that the review conveyed overall positive
or negative sentiment. Pang et al. [134] achieved 82.9% accuracy at classifying movie
reviews as positive or negative using Support Vector Machine classification with a
simple bag-of-words feature set. In a bag-of-words technique, the classifier identifies
single-word opinion clues and weights them according to their ability to help classify
reviews as positive or negative.
While 82.9% accuracy is a respectable result for this task, there are many
aspects of sentiment that the bag-of-words representation cannot cover. It cannot
account for the effect of the word “not,” which turns formerly important indicators
of positive sentiment into indicators of negative sentiment. It also cannot account
for comparisons between the product being reviewed and other products. It cannot
account for other contextual information about the opinions in a review, like recognizing
that the sentence “The Lost World was a good book, but a bad movie”
contributes a negative opinion clue when it appears in a movie review of the Steven
Spielberg movie, but contributes a positive clue when it appears in a review of the
Michael Crichton novel. It cannot account for opinion words set off with modality or
a subjunctive (e.g. “I would have liked it if this camera had aperture control.”) In
order to work with these aspects of sentiment and enable more complicated sentiment