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| | == Summary == | | == Summary == |
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| − | Key Ideas
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| − | There are mainly three subtasks related to opinion mining. First, to determine if the text has subjective expressions or is it objective. A given text can belong to either subjective class or objective class. This is coined as SO-polarity. Second, to determine if the subjective
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| − | text expresses a positive or negative opinion. This is coined as PN-polarity. Third, to determine the strength of positive or negative opnion.
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| − | Previous work of assigning the PN polarity has been limited to confidence of assigned label. In this paper the
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| − | score is the strength of positive or negative opinion as compared to the confidence of the positive netiave label.
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| − | the grading approach allows scope for subtle subjectivity in words naunces. Where as the previous approaches assigned a hard binary
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| − | labels.
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| − | In the previous paper the authors solve the problem of determining whether a sentence is subjective or not and if it is subjective does it
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| − | have the postive or negative polarity. The combined problem is tougher than just determining the polarity of a
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| − | sentence.
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| − | determining setence subjectivity by performing binary text categorization under categories
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| − | Objective and Subjective (Pang and Lee, 2004; Yu and Hatzivassiloglou, 2003);
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| − | to aid these tasks, recent work has tackled the issue of identifying the orirentaiton of subjective terms
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| − | contained in text. i.e determine whether a term that carries opinoanted content has a positive or negative conotation
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| − | this is the key component in identifying the oreintation of the documents. There has been combined approaches to solve the above problem
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| − | but they lack realism that
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| − | The very first work in determining the term orientation has been to determine the orientatino of subjective
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| − | adjectives extracted from large unlabelled document set.The underlying intuition is that the act of conjoining adjectives is subject to linguistic constraints
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| − | on the orientation of the adjectives involved; e.g. and usually conjoins adjectives of equal orientation, while but conjoins adjectives of opposite
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| − | orientation. The authors generate a graph where terms are nodes connected by “equal-orientation” or “opposite-orientation” edges, depending on the
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| − | conjunctions extracted from the document set. A clustering algorithm then partitions the graph into
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| − | a Positive cluster and a Negative cluster, based on a relation of similarity induced by the edges
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| | == Brief description of the method == | | == Brief description of the method == |
