1. Mining and Summarizing Customer Reviews Minqing Hu and Bing Liu University of Illinois SIGKDD 2004

2. Abstract Mining product features that have been commented on by customers Identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative Summarizing the results

3. Introduction

4. Related Work Sentence vs. review Combined features Subjective Genre Classification (document level vs. sentence level, features) Sentiment classification (document level vs. sentence level, features) Text summarization (structural, key features vs. similarities and differences of reviews) Terminology finding (fix too many non-terms or miss low frequency terms)

5. System architecture

6. Frequent Features Identification The pictures are very clear. While light, it will not easily fit in pockets. (size of camera) Association mining: miner CBA (frequent more than 1% of the review sentences.)

7. Frequent Features Pruning Compactness pruning: –Features contain at least 2 words. –Features appear together in a specific order Redundancy pruning: –P-support(pure support): number of sentences this feature appears in. (p-support = 3) –Prune subset of another feature phrase : life vs. battery life.

8. Opinion Word Extraction Extract nearby adjectives of frequent features. Use Wordnet synonyms and antonyms, 30 seeds. Discard adjectives not in Wordnet

9. Orientation Identification for Opinion Words

10. Infrequent Feature Identification The pictures are absolutely amazing. The software that comes with it is amazing. Find the nearby noun/noun phrase of opinion words. May find features irrelevant to the product (15-20% infrequent feature in total)

11. Predicting the Orientations of Opinion Sentences Use the dominant orientation Use average orientation of effective opinions Use the orientation of the previous opinion sentence “But” for sentiment change Negation word of “no”, “not”, “yet”, distance set to 5

12. Summary Generation See the previous slide

13. Evaluation (1) The effectiveness of feature extraction

14. Evaluation (2) The effectiveness of opinion sentence extraction.

15. Evaluation (3) The accuracy of orientation prediction of opinion sentences.

16. Conclusion Mining and summarizing product reviews Useful for both shoppers and manufacturers. Pronoun resolution Strength of opinions Adverbs, verbs and nouns opinions Monitoring customer reviews (novelty)