Using morphological and syntactic structures for Chinese opinion analysis

Lun Wei Ku; Ting Hao Huang; Hsin Hsi Chen

doi:10.3115/1699648.1699672

Using morphological and syntactic structures for Chinese opinion analysis

Lun Wei Ku, Ting Hao Huang, Hsin Hsi Chen

College of Information Sciences and Technology

Research output: Contribution to conference › Paper › peer-review

46 Scopus citations

Abstract

This paper employs morphological structures and relations between sentence segments for opinion analysis on words and sentences. Chinese words are classified into eight morphological types by two proposed classifiers, CRF classifier and SVM classifier. Experiments show that the injection of morphological information improves the performance of the word polarity detection. To utilize syntactic structures, we annotate structural trios to represent relations between sentence segments. Experiments show that considering structural trios is useful for sentence opinion analysis. The best f-score achieves 0.77 for opinion word extraction, 0.62 for opinion word polarity detection, 0.80 for opinion sentence extraction, and 0.54 for opinion sentence polarity detection.

Original language	English (US)
Pages	1260-1269
Number of pages	10
DOIs	https://doi.org/10.3115/1699648.1699672
State	Published - 2009
Event	2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009 - Singapore, Singapore Duration: Aug 6 2009 → Aug 7 2009

Conference

Conference	2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009
Country/Territory	Singapore
City	Singapore
Period	8/6/09 → 8/7/09

All Science Journal Classification (ASJC) codes

Computational Theory and Mathematics
Computer Science Applications
Information Systems

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10.3115/1699648.1699672

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title = "Using morphological and syntactic structures for Chinese opinion analysis",

abstract = "This paper employs morphological structures and relations between sentence segments for opinion analysis on words and sentences. Chinese words are classified into eight morphological types by two proposed classifiers, CRF classifier and SVM classifier. Experiments show that the injection of morphological information improves the performance of the word polarity detection. To utilize syntactic structures, we annotate structural trios to represent relations between sentence segments. Experiments show that considering structural trios is useful for sentence opinion analysis. The best f-score achieves 0.77 for opinion word extraction, 0.62 for opinion word polarity detection, 0.80 for opinion sentence extraction, and 0.54 for opinion sentence polarity detection.",

author = "Ku, {Lun Wei} and Huang, {Ting Hao} and Chen, {Hsin Hsi}",

year = "2009",

doi = "10.3115/1699648.1699672",

language = "English (US)",

pages = "1260--1269",

note = "2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009 ; Conference date: 06-08-2009 Through 07-08-2009",

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Using morphological and syntactic structures for Chinese opinion analysis. / Ku, Lun Wei; Huang, Ting Hao; Chen, Hsin Hsi.
2009. 1260-1269 Paper presented at 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009, Singapore, Singapore.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Using morphological and syntactic structures for Chinese opinion analysis

AU - Ku, Lun Wei

AU - Huang, Ting Hao

AU - Chen, Hsin Hsi

PY - 2009

Y1 - 2009

N2 - This paper employs morphological structures and relations between sentence segments for opinion analysis on words and sentences. Chinese words are classified into eight morphological types by two proposed classifiers, CRF classifier and SVM classifier. Experiments show that the injection of morphological information improves the performance of the word polarity detection. To utilize syntactic structures, we annotate structural trios to represent relations between sentence segments. Experiments show that considering structural trios is useful for sentence opinion analysis. The best f-score achieves 0.77 for opinion word extraction, 0.62 for opinion word polarity detection, 0.80 for opinion sentence extraction, and 0.54 for opinion sentence polarity detection.

AB - This paper employs morphological structures and relations between sentence segments for opinion analysis on words and sentences. Chinese words are classified into eight morphological types by two proposed classifiers, CRF classifier and SVM classifier. Experiments show that the injection of morphological information improves the performance of the word polarity detection. To utilize syntactic structures, we annotate structural trios to represent relations between sentence segments. Experiments show that considering structural trios is useful for sentence opinion analysis. The best f-score achieves 0.77 for opinion word extraction, 0.62 for opinion word polarity detection, 0.80 for opinion sentence extraction, and 0.54 for opinion sentence polarity detection.

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M3 - Paper

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T2 - 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009

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ER -

Using morphological and syntactic structures for Chinese opinion analysis

Abstract

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