opinion digger: an unsupervised opinion miner from unstructured product reviews samaneh moghaddam,...

Opinion Digger: An Unsupervised Opinion Miner from Unstructured Product

ReviewsSamaneh Moghaddam, Martin Ester

CopyRight@luzhonghao

Lab for Internet Software TechnologiesLab for Internet Software Technologies

Abstract

• Propose an unsupervised method for aspect extraction from unstructured reviews using known aspects.

• Introduce an unsupervised method for aspect rating(on a scale from 1 to 5)based on the rating guideline.

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Problem Definition

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Known aspects

Output aspects

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Aspect Extraction

• Finding frequent noun phrases

• Mining opinion patterns

• Filtering out non-aspects

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Finding Frequent Noun Phrases

• Hypothesis:those nouns that are frequent noun phrases as a set of potential aspects

• Apply Apriori algorithm to find all multi-part noun phrases which are frequent.(support value=1%)

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Mining Opinion Patterns

• Finds matching phrases for each of the known aspects.It searches for each known aspect in the reviews and finds its nearest adj. in that sentence segment as corresponding sentiment.

• Saved the matching phrase and picks the POS tags of all words as a pattern.

• After mining all POS patterns, use GSP(Generalized Sequential Pattern) to find frequent patterns(sup=1%)

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Filtering out non-aspects

• Pnum: whitch is the number of opinion patterns that are matched at least once by the potential aspect.

• If pnum < 2: eliminate the aspect

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Compute Aspect Rating

• For each aspect,extracts the nearest adj. as its sentiment.

• For each sentiment of the every aspect of a product,search in the WordNet synonymy graph to find two rated synonyms from the rating guideline .(can see them from the website Epinion.com)

• Compute the rating of each sentment8

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Sentiment rating space

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Compute each sentiment

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Experimental results

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Evaluation of Aspect Extraction

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Thank you!

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