1. Samaneh Moghaddam, Martin Ester
Opinion Digger: An Unsupervised Opinion Miner from Unstructured Product Reviews
Samaneh Moghaddam, Martin Ester

2. 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.

3. Problem Definition
Known aspects
Output aspects

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

5. 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%)

6. 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%)

7. 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

8. 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

9. Sentiment rating space

10. Compute each sentiment

11. Experimental results

12. Evaluation of Aspect Extraction

13. Thank you!