Collecting Evaluative Expression for Opinion Extraction Nozomi Kobayasi, Kentaro Inui, Yuji Matsumoto (Nara Institute) Kenji Tateishi, Toshikazu Fukushima.

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Presentation transcript:

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. –

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 Take orientation as a special type of Value (I like the lether seats of Product_X) of is

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: is Sentences: –… is and … –… is … 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/suppor t/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 also 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 different domains. Future work: directly extract triplets from Web.