[Purpose/significance] Existing methods of extracting keywords can't be applied to the social Q&A community effectively, because they are not suitable for the characteristics of the social Q&A community which embodies short texts, colloquial contents and sparse data. They rarely think about the impact of users' attention on words. In view of the aforementioned problem, this paper presents a novel keywords extraction method based on multi-attributes weighted for the social Q&A community. [Method/process] This method improved the traditional TF-IDF algorithm by introducing the tuning function and the part of speech. Besides, it calculated the weight of words based on a linear weighting formula, which fused four attributes of user focus by dealing with numbers of users' answer, attention, browse, and comments. [Result/conclusion] Experiments show that this method can extract keywords from the social Q&A community more effectively.
Yu Bengong
,
Li Ting
,
Yang Ying
. Keywords Extraction Method for the Social Q&A Community Based on Multi-attributes Weighted[J]. Library and Information Service, 2018
, 62(5)
: 132
-139
.
DOI: 10.13266/j.issn.0252-3116.2018.05.015
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