Mei gao practicedemo_2
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This document discusses using latent Dirichlet allocation (LDA) to classify restaurants from online reviews into topics and recommend restaurants to users. It shows that LDA reduces the feature dimension by over 99% compared to bag-of-words, improves computation efficiency by over 90%, and achieves a classification accuracy between 73-81% depending on the model. LDA identifies 15 topics from reviews including Japanese, Mexican, brunch, bar atmosphere, pizza, Indian, Asian, fast food, sweets, BBQ, compliments, and service. It also evaluates recommendation performance using a normalized distance measure.
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Mei gao practicedemo_2
- 3. Topic0 japanese Topic1 mexican Topic2 brunch Topic3 bar/atmosphe re Topic4 pizza Topic 5 compliment sushi tacos breakfast great pizza great roll mexican coffee beer crust best pita salsa eggs happyhour wings love tuna burrito bacon bar thin good salmon chips pancakes drinks pepperoni like Topic6 indian Topic7 asian Topic8 fastfood Topic9 sweets Topic10 bbq Topic 11 service(bad) Indian Thai burger bagels cheese service buffet Pho fries cheese bbq didn’t masala Chinese potato best sauce never naan soup onion ring smoothies chicken even bianco curry dog Iove ribs back LDA (Latent Dirichlet Allocation) 12 topics
- 4. Good restaurant: average star>3.5 Bad restaurant: average star<=3.5 Classification : Weight for each topic Classifier Linear SVM Logistic Regression Random Forest Accuracy in Cross Validation 73.67% 81.19% 77.7%
- 5. Evaluation of Recommendation Error Using Normalized Distance-based Performance Measure (NDPM) Rank by recommendation Rank by user's actual ratings Restaurant_1 Restaurant_1 Restaurant_2 Restaurant_3 Restaurant_3 Restaurant_7 Restaurant_4 Restaurant_2 Restaurant_5 Restaurant_4 Restaurant_6 Restaurant_9 Restaurant_7 Restaurant_8 Restaurant_8 Restaurant_5 Restaurant_9 Restaurant_10 Restaurant_10 Restaurant_6
- 6. Deep hierarchical abstraction Learning structure of images Deep learning for image ranking
- 7. Mei Gao
- 8. Assessment of LDA BOW (Bag of Words) LDA Feature Dimension 10000 words in dictionary 15 topics >99% dimension reduction Computation Efficiency 2.5 hrs 15 min >90% computation time (2000 samples) (10 fold cross validation)
- 9. Topic0 Topic1 Topic2 Topic3 Topic4 Japanese Mexican brunch Bar/ pizza Atmosphere sushi tacos breakfast great pizza roll mexican coffee beer crust pita salsa eggs happyhour wings tuna burrito bacon bar thin salmon chips pancakes drinks pepperoni Topic5 Topic6 Topic7 Topic8 Topic9 Indian Asian fastfood sweets bbq Indian Thai burger bagels cheese buffet Pho fries cheese bbq masala Chinese potato best sauce naan soup Onion ring smoothies chicken bianco curry dog Iove ribs LDA (Latent DiriChlet Allocation) 15 topics Topic 10: Compliment Great, best, live, good, like Topic 11: Service ( Bad) Service, didn’t, never, even, back
- 10. Assessment of LDA Dimension Reduction: 99% reduction in dimension BOW (bag of words) features: 10,000 LDA features: 15 Topics Computation efficiency: 2000 samples, 10 fold cross validation BOW features: 2.5 hrs LDA features: 15 min
- 11. 3% 4% 10% 4% 5% 29% 20% 6% 3% 16% Percentage others Japanese Mexican Brunch bar Service compliment Asian fastfood bbq