The effect of aggregation methods on sentiment classification in Persian reviews

被引：16

作者：

Basiri M.E. ^{[1
]}

Kabiri A. ^{[1
]}

Abdar M. ^{[2
]}

Mashwani W.K. ^{[3
]}

Yen N.Y. ^{[4
]}

Hung J.C. ^{[5
]}

机构：

[1] Department of Computer Engineering, Shahrekord University, Shahrekord

[2] Department of Computer Science, University of Quebec in Montreal, Montreal, QC

[3] Department of Mathematics, Kohat University of Science and Technology, Kohat

[4] School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu

[5] Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung

来源：

Enterprise Information Systems | 2020年 / 14卷 / 9-10期

关键词：

aggregation methods; lexicon-based method; Persian language processing; Sentiment analysis;

D O I：

10.1080/17517575.2019.1669829

中图分类号：

学科分类号：

摘要：

One of the most essential parts of every sentiment analysis application is the aggregation mechanism used to combine results obtained from a lower granularity level into an overall result. In this paper, the effects of the sentiment lexicon, aggregation level, and aggregation method on the sentiment polarity and rating classification of Persian reviews are investigated. To this aim, a new sentiment aggregation method based on the cross-ratio operator is proposed. The results on four Persian review data sets show that the review-level aggregation can improve rating classification, although this approach does not have a positive impact on polarity classification. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

引用

页码：1394 / 1421

页数：27

共 66 条

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