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Subjective Document Classification Using Network Analysis

Authors: Minkyoung Kim; June-Sup Lee; Byoung-Tak Zhang;

Subjective Document Classification Using Network Analysis

Abstract

Network analysis methods have been applied in many areas such as computer science, social science, biology and physics. In this paper, we apply network analysis methods to the linguistic domain for classifying subjective documents. Particularly, we view that subjective documents are related to one another according to some common subjective words and build a subjective document network of which nodes are documents and of which links represent the similarity between two documents. In addition, we consider that adjectives and adverbs are the two representatives carrying sentimental polarities among parts-of-speeches, and perform experiments for three cases, using adjectives only, adverbs only, and both adjectives and adverbs together. In conclusion, this paper proposes a new method to the subjective document classification problem by applying network analysis methods without requiring linguistic domain knowledge and suggests the possibility of detecting themes among documents rather than binary classification.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Average
Average
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