Please use this identifier to cite or link to this item:
Title: Effectiveness of simple linguistic processing in automatic sentiment classification of product reviews
Authors: Zhou, Yunyun
Khoo, Christopher S. G.
Na, Jin-Cheon
Sui, Haiyang
Chan, Syin
Keywords: Communication and Information
Issue Date: 2004
Source: Na, J.-C., Sui, H., Khoo, C. S. G., Chan, S., & Zhou, Y. (2004). Effectiveness of simple linguistic processing in automatic sentiment classification of product reviews. In I.C. McIlwaine (Ed.), Knowledge Organization and the Global Information Society: Proceedings of the Eighth International ISKO Conference (pp. 49-54). Wurzburg, Germany: Ergon Verlag.
Abstract: This paper reports a study in automatic sentiment classification, i.e., automatically classifying documents as expressing positive or negative sentiments/opinions. The study investigates the effectiveness of using SVM (Support Vector Machine) on various text features to classify product reviews into recommended (positive sentiment) and not recommended (negative sentiment). Compared with traditional topical classification, it was hypothesized that syntactic and semantic processing of text would be more important for sentiment classification. In the first part of this study, several different approaches, unigrams (individual words), selected words (such as verb, adjective, and adverb), and words labeled with part-of-speech tags were investigated. A sample of 1,800 various product reviews was retrieved from Review Centre ( for the study. 1,200 reviews were used for training, and 600 for testing. Using SVM, the baseline unigram approach obtained an accuracy rate of around 76%. The use of selected words obtained a marginally better result of 77.33%. Error analysis suggests various approaches for improving classification accuracy: use of negation phrase, making inference from superficial words, and solving the problem of comments on parts. The second part of the study that is in progress investigates the use of negation phrase through simple linguistic processing to improve classification accuracy. This approach increased the accuracy rate up to 79.33%.
Rights: © 2004 International ISKO Conference. This is the author created version of a work that has been peer reviewed and accepted for publication by Proceedings of the Eighth International ISKO Conference, International ISKO Conference. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [URL:].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:WKWSCI Conference Papers

Files in This Item:
File Description SizeFormat 
na_sui_khoo.sentiment_classification.ISKO2004.pdf154.44 kBAdobe PDFThumbnail

Page view(s) 5

Updated on Sep 25, 2022

Download(s) 5

Updated on Sep 25, 2022

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.