Automatically Collect the Adjectives and Calculate their Sentiment Intensity in the Sentiment Analysis

Article Preview

Abstract:

In order to automatically expand high quality polarity adjectives as much as possible by using the WordNet, three types of query strings are used in this study. Among them, the third type of query string is the most important. Besides ‘synonym’ and ‘antonym’ relations, we also use ‘similar to’ and ‘also see’ relations of the WordNet. In fact, the last two relations are more important than the first two ones. With the help of the newly proposed method, we can calculate the sentiment intensity of those expanded adjectives exactly. Abundant experiments demonstrate that our improved POAE algorithm is very effective in expanding and ranking adjective seed list in the sentiment analysis.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1138-1142

Citation:

Online since:

October 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] B. Pang, L. Lee. Foundations and trends in information retrieval, 2008, 2(1-2): 1-135.

Google Scholar

[2] Minqing Hu and B. Liu. Mining and Summarizing Customer Reviews. Proceedings of the ACM SIGKDD, Aug 22-25, 2004, Seattle, Washington, USA.

Google Scholar

[3] S. Blair-Goldensohn, K. Hannan, R. McDonald, et al. Building a sentiment summarizer for local service reviews[C]/WWW Workshop on NLP in the Information Explosion Era. 2008: 14.

Google Scholar

[4] A. Andreevskaia and S. Bergler. Mining WordNet for a fuzzy sentiment: Sentiment tag extraction from WordNet glosses. Proceedings of the European Chapter of EACL, (2006).

Google Scholar

[5] A. Esuli and F. Sebastiani. PageRanking WordNet synsets: An application to opinion mining. in Proceedings of the Association for Computational Linguistics (ACL), (2007).

Google Scholar

[6] J. Kamps, M. Marx, R. Mokken and M. de Rijke. Using WordNet to measure semantic orientation of adjectives. In Proc. Of LREC-2004, pp: 1115-1118, (2004).

Google Scholar

[7] S. -M. Kim and E. Hovy. Determining the sentiment of opinions. Proceedings of the International Conference on Computational Linguistics (COLING), (2004).

DOI: 10.3115/1220355.1220555

Google Scholar

[8] Peter D. Turney. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th ACL, pp: 417-424, (2002).

DOI: 10.3115/1073083.1073153

Google Scholar

[9] S. Banerjee and T. Pedersen. An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet. Proceedings of the Fourth International CICLING. Mexico City, Mexico, (2002).

DOI: 10.1007/3-540-45715-1_11

Google Scholar

[10] W. Hui and M. Jun. Journal of computational information systems, 2012, 8(16): 6569-6577.

Google Scholar

[11] B. Liu, Minqing Hu and Junsheng Cheng. Opinion Observer: Analyzing and Comparing Opinions on the Web. Proceedings of the WWW-2005, May 10-14, 2005, Chiba, Japan.

DOI: 10.1145/1060745.1060797

Google Scholar

[12] Theresa Wilson, Janyce Wiebe and Paul Hoffmann. Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. Proceedings of HLT/EMNLP 2005, Vancouver, Canada.

DOI: 10.3115/1220575.1220619

Google Scholar