Method of Subjective Lexicon Creation for Chinese Sentiment Analysis

Jing Zhang; Hao Jin

doi:10.4028/www.scientific.net/AMM.34-35.801

Paper Titles

Dynamic Buckling of Composite Bars Subject to Axial Impact
p.780

Decomposition Model Based on Optimal Power Flow in Interconnected Electricity Networks
p.785

Study on Co-KZr₂(PO₄)₃-Type Crystalline Purple Ceramic Pigments
p.790

New Method for Pipeline Leakage Measurement in Pneumatic Industry
p.795

Method of Subjective Lexicon Creation for Chinese Sentiment Analysis
p.801

Method of the Safety Early Warning on Hazard Installations
p.806

Sealing Structure Design and Analysis of Non-API Pipe Connection
p.811

Study on the Enrichment Degree of Coalbed Methane Based on the Fuzzy Comprehensive Evaluation
p.815

FEM Simulation on the Structural Reliability of Beam Pumping Unit Based on the Methods of Monte-Carlo
p.820

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 34-35Method of Subjective Lexicon Creation for Chinese...

Method of Subjective Lexicon Creation for Chinese Sentiment Analysis

Abstract:

The lack of Chinese subjective lexicon in the study and application of the subjective and emotional information restricts the further development of subjectivity analysis of Chinese words. Based on the current situation of and demand for Chinese sentiment analysis, the method of automatically obtaining subjective lexicon from the target corpus is studied. A creation method of subjective lexicon is presented, the subjective lexicon and language model are defined, a self-adaptive subjectivity bootstrapping algorithm and the characteristic model of subjectivity attribute are designed, and all these lead to the realization of the automatic judgment of sentiment polarity, subjectivity intensity and the subjectivity and objectivity of a word in the subjectivity entry. Experiments prove that by using machine learning the proposed method of automatic creation of Chinese subjective lexicon is highly efficient and with excellent performance.

You might also be interested in these eBooks

Mechanical Engineering and Green Manufacturing

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 34-35)

Pages:

801-805

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.34-35.801

Citation:

Cite this paper

Online since:

October 2010

Authors:

Jing Zhang, Hao Jin

Keywords:

Algorithm (GA), Automatic Judgment, Character Choice, Chinese Subjective Lexicon, Creation Method, Machine Learning (ML), Model

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] SONG Hong-yan, LIU Jun, YAO Tian-fan: Construction of an Annotated Corpus for Chinese Opinioned-Subjective Texts. Journal of Chinese Information Processing, Vol. 23, pp.123-128(2009).

Google Scholar

[2] Kanayama, Hiroshi, Tetsuya Nasukawa: Fully automatic lexicon expansion for domain-oriented sentiment analysis. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Sydney, Australia, pp.355-363 (2006).

DOI: 10.3115/1610075.1610125

Google Scholar

[3] Soo-Min Kim, Eduard Hovy: Identifying and analyzing judgment opinions. Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics. New York City, USA, pp.200-207(2006).

DOI: 10.3115/1220835.1220861

Google Scholar

[4] Banea C., Mihalcea R. , Wiebe J: A bootstrapping method for building subjectivity lexicons for languages with scarce resources. Proceedings of the Learning Resources Evaluation Conference. Marrakech, Morocco, pp.2764-2767 (2008).

Google Scholar

[5] Valentin Jijkoun, Katja Hofmann: Task-based Evaluation Report: Building a Dutch Subjectivity Lexicon. ILPS - ISLA, University of Amsterdam, 2 September(2008).

Google Scholar

[6] Janyce Wiebe, Rada Mihalcea: Word sense and subjectivity. Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics. Sydney, NSW, Australia, pp.1065-1072(2006).

DOI: 10.3115/1220175.1220309

Google Scholar

[7] Wilson, Theresa Ann: Fine-grained Subjectivity and Sentiment Analysis: Recognizing the intensity, polarity, and attitudes of private states. Dissertation, School of Arts and Sciences, University of Pittsburgh (2008).

Google Scholar

[8] Michael Collins: Head-Driven Statistical Models for Natural Language Parsing. Computational Linguistics, Vol. 29, pp.589-637(2003).

DOI: 10.1162/089120103322753356

Google Scholar

[9] Eugene Charniak: A maximum-entropy-inspired parser. Proceedings of the First Meeting of the North American Chapter of the Association for Computational Linguistics. Seattle, WA, USA, pp.132-139(2000).

Google Scholar

[10] Peter Turney: Thumps up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. Philadelphia, Pennsylvania, pp.417-424 (2002).

DOI: 10.3115/1073083.1073153

Google Scholar