Collecting Evaluative Expression for Opinion Extraction

Nozomi Kobayasi, Kentaro Inui, Yuji Matsumoto (Nara Institute)

Kenji Tateishi, Toshikazu Fukushima

(NEC Internet System Lab)

IJCNLP 2004

Lun Wei Ku, 2005/04/21

What are they going to do?

The seats are very comfortable and supportive. But the back seat room is tight.

– <Product_X, seats, comfortable>– <Product_X, seats, supportive>– <Product_X, back seat room, tight>

Related Work

• Classify reviews into recommended or not recommended.

• Positive sentences and negative sentences.

• Acquiring subjective words – adjectives, nouns, verbs and adverbs.

• Using patterns

Related Work

1. Bing Liu, Minqing Hu and Junsheng Cheng. "Opinion Observer: Analyzing and Comparing Opinions on the Web" To appear in Proceedings of the 14th international World Wide Web conference (WWW-2005), May 10-14, 2005, in Chiba, Japan.

2. Mining and summarizing customer reviews". Proceedings of the ACM SIGKDD 2004

Attribute and Value

• <Subject, Attribute, Value>

• Take orientation as a special type of Value

(I like the lether seats of Product_X)

• <Attribute> of <Subject> is <Value>

Collecting Expressions

Iterate the following two steps:• Candidate generation:

– Web documents– Coocurrence patterns– Subject/attribute/value dictionary– Coocurrence

• Candidate selection:– Human judge– Update dictionaries

Collecting Expressions -- Example

• Pattern: <Attribute> is <Value>

• Sentences:– …<the handling> is <excellent> and …– …<the gas mileage> is <great>…

Provide only highly ranked candidates to the human judge.

Experiment Resources

• Domain: cars and video games

• 15,000 reviews (230,000 sentences) for cars and 9,700 reviews (90,000 sentences) for games.

• Dictionaries:– Subject: 389 for cars (“BMW”,”TOYOTA”)

and 660 for games (“Dark Chronicle”, “Seaman”)

Experiment Resources

– Attribute: 7 for both domains. (cost/price/service/performance/function/support/design)

– Value: using thesaurus, 247 mostly adjectives. (good/beautiful/bright/like/favorite/high)

– Patterns: select 8 patterns, decide which pattern to use according to POS. Scores are given to these patterns.

Results

Discussions

• No convergence: compound expressions

• Coverage: 45% (car) , 35% (game)

Discussions

• Value patterns outperformed attribute patterns.– Value coocurrs with not only attributes, but als

o named entities and general nouns.– There are problems in deciding attribute scope.

• Character

• Face character

• Motion character

Discussions

Conclusions

• A semi-automatic methods based on cooccurrence patterns of subjects, attributes and values.

• More efficiently than manual collection.• Cooccurrence patterns works well across dif

ferent domains.• Future work: directly extract triplets <Subje

ct, Attribute, Value> from Web.