Samaneh Moghaddam, Martin Ester Opinion Digger: An Unsupervised Opinion Miner from Unstructured Product Reviews Samaneh Moghaddam, Martin Ester CopyRight@luzhonghao

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.

Problem Definition Known aspects Output aspects

Aspect Extraction Finding frequent noun phrases Mining opinion patterns Filtering out non-aspects

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%)

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%)

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

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 sentment

Sentiment rating space

Compute each sentiment

Experimental results

Evaluation of Aspect Extraction

Thank you!