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Retrieval of Personal Web Documents by Extracting Subjective Expressions

Authors: Koji Abe; Rikio Onai; Takahiro Hayashi;

Retrieval of Personal Web Documents by Extracting Subjective Expressions

Abstract

This paper presents a method for gathering Japanese Web documents which contain personal opinions. Our method is available as a pre-processing of applications for mining various opinions. In order to find personal documents on the Web, we focus on four kinds of subjective expressions: (1) negative meaning expressions, (2) final particles, (3) interjections, and (4) specific symbols such as face marks. Measuring the frequencies of these subjective expressions in a document, our method classifies Web documents into personal and non-personal ones. Besides, our method gives the documents scores which show the accuracy of the classification results. We experimentally confirmed the effectiveness of the proposal using 1200 Web documents. The experimental results have shown the precision and recall of the proposed classification are 0.70 and 0.87, respectively. In addition, we have confirmed that personal documents can be easily obtained by gathering documents which are given high scores.

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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!
2
Average
Average
Average
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