Using term similarity measures for classifying short document data
by Hirohisa Seki; Shuhei Toriyama
International Journal of Computational Intelligence Studies (IJCISTUDIES), Vol. 10, No. 2/3, 2021

Abstract: Term expansion (a.k.a. document expansion), proposed by Carpineto et al., is a method used for text classification. When handling short text data like social media and blogs, we can apply the term expansion method to expand the sparse information in them. While the prior works on term expansion use an formal concept analysis (FCA)-based similarity measure defined between terms (or words), this paper studies the effectiveness of using two kinds of measures for term expansion: one is weighted similarity measures studied in FCA, and the other is some correlation measures, like cosine and all-conf, often employed in data mining. We also present some properties on the relationship between these term similarity/correlation measures and the notion of relevancy in classification. We show empirically that cosine correlation measure outperforms the prior methods in our two short document data. We also make a comparison of our approach with an latent Dirichlet allocation (LDA)-based term expansion approach by Rogers et al.

Online publication date: Wed, 02-Jun-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Intelligence Studies (IJCISTUDIES):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com