eprintid: 2320
rev_number: 9
eprint_status: archive
userid: 286
dir: disk0/00/00/23/20
datestamp: 2017-01-13 02:35:30
lastmod: 2017-12-04 09:31:53
status_changed: 2017-01-13 02:35:30
type: conference_item
metadata_visibility: show
creators_name: Pham, Thi Ngan
creators_name: Tran, Van Hien
creators_name: Nguyen, Tri Thanh
creators_name: Ha, Quang Thuy
creators_id: nganpt.di12@vnu.edu.vn
creators_id: ntthanh@vnu.edu.vn
creators_id: thuyhq@vnu.edu.vn
title: Exploiting Distance graph and Hidden Topic Models for Multi-label Text Classification
ispublished: pub
subjects: IT
divisions: fac_fit
abstract: Hidden topic models, the method to automatically detect the topics which are (hidden in a text) represented by words, have been successfully in many text mining tasks including text classification. They help to get the semantics of text by abstracting the words in text into topics. Another new method for text representation is distance graph model, which has the ability of preserving the local order of words in text, thus, enhancing the text semantics. This paper proposes a method to combine both hidden topic and distance graph models for opinion mining in hotel review domain using multi-label classification approach. Experiments show the efficiency of the proposed model provides a better performance of 4% than that of the baseline.
date: 2017-04
date_type: published
full_text_status: none
pres_type: paper
event_title: The 9th Asian Conference on Intelligent Information and Database Systems
event_location: Kanazawa, Japan
event_dates: 3-5 April 2017
event_type: conference
refereed: TRUE
citation:   Pham, Thi Ngan and Tran, Van Hien and Nguyen, Tri Thanh and Ha, Quang Thuy  (2017) Exploiting Distance graph and Hidden Topic Models for Multi-label Text Classification.  In: The 9th Asian Conference on Intelligent Information and Database Systems, 3-5 April 2017, Kanazawa, Japan.