Adaptation and Use of Subjectivity Lexicons for Domain Dependent Sentiment Classification

被引：17

作者：

Dehkharghani, Rahim ^{[1
]}

Yanikoglu, Berrin ^{[1
]}

Tapucu, Dilek ^{[1
]}

Saygin, Yucel ^{[1
]}

机构：

[1] Sabanci Univ, Dept Comp Sci & Engn, Istanbul, Turkey

来源：

12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2012) | 2012年

关键词：

opinion mining; sentiment analysis; polarity extraction; SentiWordNet; lexicon based methods; machine learning;

D O I：

10.1109/ICDMW.2012.121

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Sentiment analysis refers to the automatic extraction of sentiments from a natural language text. We study the effect of subjectivity-based features on sentiment classification on two lexicons and also propose new subjectivity-based features for sentiment classification. The subjectivity-based features we experiment with are based on the average word polarity and the new features that we propose are based on the occurrence of subjective words in review texts. Experimental results on hotel and movie reviews show an overall accuracy of about 84% and 71% in hotel and movie review domains respectively; improving the baseline using just the average word polarities by about 2% points.

引用

下载

页码：669 / 673

页数：5

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