Feature-based sentiment analysis approach for product reviews

被引：13

作者：

Wang, Hanshi ^{[1
]}

Liu, Lizhen ^{[1
]}

Song, Wei ^{[1
]}

Lu, Jingli ^{[2
]}

机构：

[1] College of Information Engineering, Capital Normal University, Beijing

来源：

Journal of Software | 2014年 / 9卷 / 02期

关键词：

Dependency parsing; Polarity classification; Sentiment analysis;

D O I：

10.4304/jsw.9.2.274-279

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

The researches and applications of sentiment analysis become increasingly important with the rapid growth of online reviews. But traditional sentiment analysis models have been lacking in concern on the modifying relationship between words for sentiment analysis of Chinese reviews, and limit the development of opinion mining. This paper proposes a feature-based vector model and a novel weighting algorithm for sentiment analysis of Chinese product reviews. It considers both modifying relationships between words and punctuations in review texts. Specifically, it can classify reviews into two categories, i.e., positive and negative, and can also represent the sentiment strength by adverb of degree. Moreover, a novel feature extraction method based on dependency parsing is presented to identify the corresponding aspects that opinions words modify. We conduct some experiments to evaluate our algorithms, and demonstrate that the proposed approaches are efficient and promising. © 2014 Academy Publisher.

引用

页码：274 / 279

页数：5

共 22 条

[1]

Tan S., Zhang J., An empirical study of sentiment analysis for chinese documents, Expert Systems With Applications, 34, 4, pp. 2622-2629, (2008)

[2]

Wan X., Bilingual co-training for sentiment classification of Chinese product reviews, Computational Linguistics, 37, 3, pp. 587-616, (2011)

[3]

Li S., Zhou G., Wang Z., Lee S.Y.M., Wang R., Imbalanced sentiment classification, Proceedings of CIKM-2011, pp. 2469-2472, (2011)

[4]

Tang H., Tan S., Cheng X., A survey on sentiment detection of reviews, Expert Systems With Application, 36, 7, pp. 10760-10773, (2009)

[5]

Liu B., Sentiment Analysis and Subjectivity, Handbook of Natural Language Processing, pp. 627-667, (2010)

[6]

Pang B., Lee L., Vaithyanathan S., Thumbs up? Sentiment classification using machine learning techniques, Proceedings of the ACL-02 Conference On Empirical Methods In Natural Language Processing, pp. 79-86, (2002)

[7]

Mullen T., Collier N., Sentiment analysis using support vector machines with diverse information sources, Proceedings of EMNLP-2004, pp. 412-418, (2004)

[8]

Blitzer J., Dredze M., Pereira F., Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification, Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 440-447, (2007)

[9]

Pan W., Li S., Tag Ontology Automatic Building for Semantic Searching of Services: A Case Study on Mashup Services, Journal of Computers, 7, 12, pp. 2979-2986, (2012)

[10]

Prabowo R., Thelwall M., Sentiment analysis: A combined approach, Journal of Informetrics, 3, 2, pp. 143-157, (2009)

← 1 2 3 →