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In other words, relation (6) is well defined for all x 1,x 2≥0.
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Weight of words relations are obtained as S(w_{i},w_{j})=ne(w_{j},w_{i}) (6).
Weight of words relations are obtained as S(w_{i},w_{j})=ne(w_{j},w_{i}) (6) where S(w i,w j ) is the frequency of both w i and w j appears in the same words window.
Xia et al. [24] design two types of feature sets: POS based and word relation based.
Items can belong to more than one relation, which allows words to appear in the word relation and, say, the syntax relation.
Their word relation based method improves an accuracy of 87.7 and 85.15% on movie and product datasets.
Also, Xia et al. [24] improved the classification performances from 84.8 to 87.7% using their proposed word relation based feature selection method.
Russell at times used the word "relation" in a broad sense so as to include qualities, which could be considered as "monadic" relations, i.e., relations that only involve one relatum.
(You have to love the fact that they still use the word "relations". So quaint).
First, two types of feature sets are designed for sentiment classification, namely the part-of-speech based feature sets and the word-relation based feature sets.
The current study tested these assumptions by comparing different kinds of words (e.g., words versus non-words) and different kinds of word-word relations (e.g., associative versus repetition).
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