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Extracting Opinion Topics for Chinese Opinions using Dependence Grammar

Guang Qiu, Kangmiao Liu, Jiajun Bu*, Chun Chen, Zhiming Kang

Reporter: Chia-Ying Lee Advisor: Prof. Hsin-Hsi Chen

Zhejiang University 浙江大學SIGKDD Workshop on Data Mining and Audience Intelligence for Advertising

ADKDD 07

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Introduction Problem Definition: Determining opinion sentence, and

extracting the topic from a opinion sentence.

Advertisements promoting systems recommend without considering the sentiment polarity of the texts.

A reasonable advertisement should be about that of a rival or solutions to user’s complain

Kim, Soo-Min and Hovy, Eduard: Opinion is described as a quadruple including Topic, Holder, Claim and Sentiment.

Most of previous work only focuses on sentiment classification assuming topics are given in prior.

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Related Work (1/2)Sentiment Classification1.Hatzivassiloglou and McKeown, 1997

Pairs of adjectives conjoined by and, or, but, either-or, or neither-nor

2.Wiebe, 2000Focuses on subjectivity tagging which distinguishes opinion sentences

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Related Work (2/2)

3.Esuli and Sebastiani, 2005Text classification by glosses.

4.Ku, Liang, and Chen, 2006Sentiment orientation of sentences can be concluded from that of words

5.Pang, Lee and Vaithyanathan, 2002Machine learning methods:Naive Bayes,

Maximum Entropy and SVM.

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Method - Acquire sentiment words (1/2)

Assumption: 1. Regard sentences with sentiment words as opinion

ones2. Topic is assured to exist in these opinion sentences

Data set of sentiment words: WS1: NTUSD

2812 positive words and 8276 negative words

D1: Emotion classification by Bruce and Wiebe1256 positive blog articles and 1238 negative

ones

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Method - Acquire sentiment words (2/2)

D2: Blog search results of Baidu 372 names of products as queries, 24146 snippets Manual label 1685 snippets of POS, NEG, NEU Select adj. correlated with of the adj. in D1. Calculate the probability of each word occurs in

each sentiment category

WS1+D1+D2: 3269 positive words and 9621 negative words

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Method - Extracting the topics using rules (1/4)

<ROLE_SENTI, RELA, ROLE_TOPIC>

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Method - Extracting the topics using rules (2/4)

1. <VOB, SIBLING, SBV>

2. <DE, GRANDPARENT-SIBLING, SBV>

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Method - Extracting the topics using rules (3/4)

3. <ATT, PARENT-SIBLING, SBV>

4.<HED, CHILD, VOB>

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Method - Extracting the topics using rules (4/4)

5. <HED, CHILD, SBV>

6. <ADV, SIBLING, VOB>

7. <ANY, NEAREST, ANYNOUN>

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Experiments and Result(1/2)Data:

Blog search results of Baidu372 queries22461 snippets unlabeled as POS, NEG, NEU51661 sentencesTwo annotators to annotate topics, and POS,

NEG, NEU570 sentences250 for sentiment and 320 for neutral

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Experiments and Result(2/2) Opinion sentence

SVM (using unigram words as the features)

Topic extraction218 sentences are correctly extracted out

of 250 opinion sentences, with the accuracy of 87.2%.

Exectly match 12

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ConclusionProposed a rule-based approach to

extracting topics in opinion sentences.

Employ a syntactic parsing on sentences and take advantage of the syntactic roles of words and their dependency relationships to extract the topic.

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Future Work Negation words Noise filtering method Co-reference resolution Enlarge current rules to cover more

situations

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HIT LTP system http://ir.hit.edu.cn/demo/ltp/ltp_v2.0.py

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Thank you!

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