Mining and Summarizing Customer Reviews

Minqing Hu and Bing Liu

University of Illinois

SIGKDD 2004

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

Introduction

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)

System architecture

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.)

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.

Opinion Word Extraction

• Extract nearby adjectives of frequent features.

• Use Wordnet synonyms and antonyms, 30 seeds.

• Discard adjectives not in Wordnet

Orientation Identification for Opinion Words

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)

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

Summary Generation

• See the previous slide

Evaluation (1)

• The effectiveness of feature extraction

Evaluation (2)• The effectiveness of opinion sentence

extraction.

Evaluation (3)

• The accuracy of orientation prediction of opinion sentences.

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)