The content is from this paper: Dependency Tree-based Sentiment
Classification using CRFs with Hidden Variables, by Tetsuji
Nakagawa.
A typical approach for sentiment classification is to use supervised
machine learning algorithms with bag-of-words as features.
A subjective sen- tence is represented as a set of words in the
sentence, ignoring word order and head-modifier relation between words. However,
sentiment classifi- cation is different from traditional topic-based text
classification. Topic-based text classification is generally a linearly
separable problem. For example, when a document con- tains some
domain-specific words, the document will probably belong to the domain. However,
in sentiment classification, sentiment polarities can be reversed. In sentiment
classification, a sentence which contains positive (or negative) polar- ity
words does not necessarily have the same polar- ity as a whole, and we need to
consider interactions between words instead of handling words indepen-
dently.
One issue of the approach to use sentence composition and machine learning is
that only the whole sentence is labeled with its polarity in general corpora for
sentiment classification, and each component of the sentence is not labeled.
Sentiment Analysis(1)-Dependency Tree-based Sentiment
Classification using CRFs with Hidden Variables