Meeting

1. Meeting • Lab. 이사 • 3월 28(토)~29(일) 잠정 예정 • 포장이사 견적 & 냉난방기 이전 설치 견적 • 정보과학회 데이터베이스 논문지1차 심사 완료 • 오타 수정 • 수식 설명 추가 요구 • STFSSD 발표자료 작성

2. A Holistic Approach toProduct Review Summarization Jung-Yeon Yang, JaeseokMyung, Sang-goo Lee Department of Computer Science and Engineering Seoul National University Center for E-Business Technology Seoul National University Seoul, Korea

3. Outline • Introduction • Related Work • Motivation • Proposed Models • Process of a Review Summarization • Feature Extraction • Sentiment Analysis • Feature Scoring • Experiment • Conclusion & Future work

4. Introduction • Product reviews • Reviews contains users’ opinion about a product • Many customers references others’ reviews when they buy some products • As a number of reviews increase, it is hard to read and grasp the whole reviews • Review Summarization • To know the whole opinions at a glance • Show the evaluation of product • Overall score about the product • Score on each representative features • An evaluation should be givenon each product feature • Opinion mining • To find user’s opinion in a text • To find representative features

5. Related Work Review Doc. • Feature extraction • frequencies of words • a structural information of sentences in a review • Sentiment analysis • Natural Language Processing (NLP)–based approach • Using a word corpus (the WordNet or the SentiwordNet) • Computational Statistics-based approach • Using a Point-wise Mutual Information (PMI) between opinion words • Feature scoring • Calculate an evaluation score of each feature • Use a sentimental score that is from the WordNet or the SentiwordNet • Use a rating score of a review document Feature Extraction SentimentAnalysis Feature Scoring Summary

6. Related Work (Cont.) < Opinion Observer > Using NLP, sentimental polarity summation < Pulse > Using Term frequencies,Clustering < RedOpal > Using rating score , based on a specific feature

7. Motivation • Problems in previous work • Workloads to extract features • Many strategies and methods • Using a word corpus • Sentiment polarities are based on general usages of words • It cannot deal with context-sensitive words (e.g. big, small, long, short, …) • Using a rating score of a review • In previous works, whole features that are extracted from the same review has the same evaluation score • Each features has to have a own evaluation score in every reviews • Challenges • A dynamic and easy method to extract features is needed. (through Tools) • We want to find out a meaning of an opinion about a feature that is modified by context-sensitive words • A better way to scoring a product feature is needed.

8. Example: using user scores of reviews 5 5 Good 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 Bad 3 3 1 1 1 1

9. Example: Considering sentimental polarities Bad Good Rating score : ★★★★ The size of camera is good to hold in one hand and comfortable. a design is so cool, nice body!!. But battery time is short. So, in outdoor, additional batteries are needed. This camera is almost perfect!!

10. Proposed Models eij eij eij • Review Model • Review Summarization Model usj stij fij stij fij fij stij Rj spij oij spij oij oij spij Ei R1 Rj Rn us1 usj usn f11 o11 st11 sp11 e11 f1j o1j st1j sp1j e1j f1n o1n st1n sp1n e1n f21 o21 st21 sp21 e21 f2j o2j st2j sp2j e2j f2n o2n st2n sp2n e2n … … … … R : review us : user score f : feature o : opinion st : strength of an opinion,sp : sentimental polarity of an opinion e : evaluation score of a feature in a review E : overall evaluation score of a feature … fi1 oi1 sti1 spi1 ei1 fij oij stij spij eij fin oin stin spin ein … … … fm1 om1 stm1 spm1 em1 fmj omj stmj spmj emj fmn omn stmn spmn emn

11. Process of a Review Summarization POS tagger Extract features Review parser Product Reviews Feature extraction Pattern rules Feature-opinion pairs Extract opinion word Word frequency N-gram Title ㅍ Classify sentiment polarity Sentiment Dictionaries Main text Sentiment analysis Sentiment polarities of Features Construct Dictionaries automatically Reviewer Review date Featureco-occurrence Derive a score of feature Feature scoring Rate Featurefrequency Evaluation scores of product features Sentiment distribution Review Summary

12. Feature Extraction • PicAChoo (Pick And Choose; a text analyzing framework) • Reducing manual efforts to obtain feature and opinion words • Enabling dynamic composition of several extraction methods • 4 primitive methods (freq., co-occurrence, sequential pattern, plug-in) • 2 composite methods (logical & arithmetical methods) • Utilizing characteristics of textual data documents Tokenized Document Composition of primitive extraction methods (freq. , co-occurrence, pattern-rules, …) Opinion Mining Preprocessing Selected Words Summarization … User Modeling

13. Sentiment Analysis • Find out sentimental polarities of opinions in reviews • Consider a context of opinion word • SO=SA(opinion word, Product category, product feature, user’s evaluation) • Point-wise Mutual Information (PMI) • A measure of association between two words (feature,opinion) (feature,opinion,polarity) positive word Dictionary Review Doc. Sentiment Analysis negative word Dictionary • Build automatically • use user scores • POS-tagging Dic.={reviewID, catID, type, POS, word, userScore, s_no, w_no}

14. Feature Scoring • Scoring strategies • Only use user score (in previous work) • Consider a distribution of sentimental polarities of user’s opinion • Use the distribution of sentimental polaritiesin the same review • Calculate evaluation scores of each feature through the adjustment of rating scores Summary = { E1, E2, … , Ei, … , Em } , m = Number of features , n = Number of reviews that contain the ith feature = number of opinions in the jth review = number of positive opinions in the jth review = number of negative opinions in the jth review F(fi, j)= frequency of fi in the jth review spij = Sentiment Polarity(fij, oij) f1 ~ fn : features R1 ~ Rn : reviewsP : positive opinion N : negative opinion

15. Experiments • Data • ePinions.com • Sentiment Analysis • Feature Scoring • Improvement of our method in comparison with a previous work • about 20%

16. Conclusion • Proposed the models • Product review model • Review summarization model • Proposed new approaches to summarize product reviews • Handle context-sensitive words in the sentiment analysis process • Feature scoring method • Utilizing user scores and sentimental polarities of opinions • Develop a text analyzing framework for feature extraction

17. weKnow uKnow iKnow Feature Extraction • Statistical approach • use Probabilities • use the POS tags • NLP approach • use Parse Trees Opinion Extraction <Feature, Opinion> Pairs • use the Sentiment Dictionary (defined by experts manually) • find out Sentimental Polarities of Features • derive scores of <Feature, Opinion> pairs • use the Sentiment Dictionaries (constructed automatically) • use Rating data of Reviews • use a PMI values between Feature and Opinion • derive the sentimental polarities Sentiment Analysis Sentiment Clause • use Rating data of Reviews • use frequencies of features • use a distribution of sentiments Feature Scoring Score Summarization Feature Score Product Summary Product Comparison Product Recommend • use the users’ profiles • use inputs from users • use Comparative Objects

18. E-mail : jyyang@europa.snu.ac.kr Intelligent Database Systems Lab. : http://ids.snu.ac.kr Thank you Q & A