NLPR at Multilingual Opinion Analysis Task in NTCIR7

This paper presents our work in the simplified Chinese opinion analysis task in NTCIR7. For identifying the subjective sentences, the domain adaptation technique was applied in our method, so that the data in NTCIR6 can be used for training subjective classifier. The evaluation results proves that the method proposed in this paper is effective. In extracting the opinion holder, we used the CRF model, which was combined with manual designed heuristics rules. For CRF model we not only extracted part-of-speech features, semantic class features, contextual features, but also some dependency features through parsing analysis. The evaluation results prove that the proposed method is effective for extracting opinion holders.

[1]  Bo Xu,et al.  Chinese named entity recognition based on multiple features , 2005, EMNLP 2005.

[2]  John Blitzer,et al.  Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification , 2007, ACL.

[3]  Eduard Hovy,et al.  Extracting Opinions, Opinion Holders, and Topics Expressed in Online News Media Text , 2006 .

[4]  Kam-Fai Wong,et al.  Opinmine - Opinion Analysis System by CUHK for NTCIR-6 Pilot Task , 2007, NTCIR.

[5]  Alistair Kennedy,et al.  SENTIMENT CLASSIFICATION of MOVIE REVIEWS USING CONTEXTUAL VALENCE SHIFTERS , 2006, Comput. Intell..

[6]  Daniel Marcu,et al.  Domain Adaptation for Statistical Classifiers , 2006, J. Artif. Intell. Res..

[7]  Hsin-Hsi Chen,et al.  Overview of Opinion Analysis Pilot Task at NTCIR-6 , 2007, NTCIR.

[8]  Qiang Yang,et al.  Transferring Naive Bayes Classifiers for Text Classification , 2007, AAAI.

[9]  Bo Pang,et al.  A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.

[10]  Janyce Wiebe,et al.  Effects of Adjective Orientation and Gradability on Sentence Subjectivity , 2000, COLING.

[11]  Fernando Pereira,et al.  Shallow Parsing with Conditional Random Fields , 2003, NAACL.

[12]  Douglas W. Oard,et al.  NTCIR-6 at Maryland: Chinese Opinion Analysis Pilot Task , 2007, NTCIR.

[13]  Hsin-Hsi Chen,et al.  Overview of Multilingual Opinion Analysis Task at NTCIR-7 , 2008, NTCIR.

[14]  Sunita Sarawagi,et al.  Domain Adaptation of Conditional Probability Models Via Feature Subsetting , 2007, PKDD.

[15]  Bo Xu,et al.  Probabilistic Parsing Action Models for Multi-Lingual Dependency Parsing , 2007, EMNLP.

[16]  Soo-Min Kim,et al.  Determining the Sentiment of Opinions , 2004, COLING.

[17]  Sabine Bergler,et al.  When Specialists and Generalists Work Together: Overcoming Domain Dependence in Sentiment Tagging , 2008, ACL.

[18]  Claire Cardie,et al.  Identifying Sources of Opinions with Conditional Random Fields and Extraction Patterns , 2005, HLT.

[19]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[20]  John Blitzer,et al.  Domain Adaptation with Structural Correspondence Learning , 2006, EMNLP.

[21]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[22]  Ellen Riloff,et al.  Creating Subjective and Objective Sentence Classifiers from Unannotated Texts , 2005, CICLing.

[23]  ChengXiang Zhai,et al.  A two-stage approach to domain adaptation for statistical classifiers , 2007, CIKM '07.

[24]  ChengXiang Zhai,et al.  Instance Weighting for Domain Adaptation in NLP , 2007, ACL.

[25]  Le Sun,et al.  ISCAS in Opinion Analysis Pilot Task: Experiments with sentimental dictionary based classifier and CRF model , 2007, NTCIR.

[26]  Songbo Tan,et al.  A novel scheme for domain-transfer problem in the context of sentiment analysis , 2007, CIKM '07.