“NLP and Computer Vision Applications in Consumer Feedback Analysis” by Olesia Khrapunova - Data Scientist at l'Oreal Beauty Tech Accelerator
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A look behind L’Oréal’s tool for consumer feedback analysis. We will discuss how NLP and Computer Vision have been applied to analyse large volumes of product reviews. Specifically, we’ll talk about topic extraction, sentiment analysis and topic enrichment in NLP, and siamese neural networks with triplet loss in Computer Vision.
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“NLP and Computer Vision Applications in Consumer Feedback Analysis” by Olesia Khrapunova - Data Scientist at l'Oreal Beauty Tech Accelerator
- 1. 1 C1 - Internal use Consumer Loop Actionable consumer feedbacks in an AI powered tool R&I
- 2. 2 C1 - Internal use Olesia Khrapunova Data Scientist at Beauty Tech Accelerator Paris @L’Oréal UVA Marketing & Business Analytics Clarabridge (now Qualtrics) Business Consultant ESSEC + CentraleSupélec Data Science & Business Analytics L’Oreal Data Scientist
- 3. 3 C1 - Internal use TECHACCELERATOR THE TECH ACCELERATOR IS OUR BEAUTY TECH TRANSFORMATION CATALYST BRINGING STRATEGIC & TECH IDEAS TO LIFE
- 4. 4 C1 - Internal use TECHACCELERATOR KEY PURPOSE • Catalyse our tech transformation • Deliver data science use cases, validated by top level, at scale • Augment our employees with tech solutions that play a strategic role for the group
- 5. 5 C1 - Internal use TECHACCELERATOR UX DESIGN DATA SCIENCE TEAM PRODUCT MANAGEMENT
- 6. 6 C1 - Internal use Promo AI BetIQ DATA POWERED BUSINESS Consumer Loop CONSUMER VOICE TrendSpotter SkinDoctor Fast & Clean Color DIGITAL FORMULATION F1 Digital Formulation GAAH • Strategic priorities • CPP validated • At scale • Products that people love • Drive change PRODUCT PORTFOLIO TECH ACCELERATORS TECH ENABLER iClosing
- 7. 7 C1 - Internal use TECHACCELERATOR CONSUMER LOOP IS AN AI WEB APPLICATION THAT TURNS CONSUMER REVIEWS INTO ACTIONABLE INSIGHTS
- 8. 8 C1 - Internal use TECHACCELERATOR CONSUMER LOOP COVERS THE MAIN BEAUTY CATEGORIES ACROSS PRIORITIZED MARKETS Four categories Make-up Skincare Perfume Haircare Nine geographies USA Mexico Brazil China Indonesia India France UK Germany
- 9. 9 C1 - Internal use TECHACCELERATOR LET’S TAKE A LOOK AT CONSUMERLOOP.BEAUTY.TECH! #
- 10. 10 C1 - Internal use TECHACCELERATOR LET’S TAKE A LOOK AT CONSUMERLOOP.BEAUTY.TECH! #
- 11. 11 C1 - Internal use TECHACCELERATOR LET’S TAKE A LOOK AT CONSUMERLOOP.BEAUTY.TECH!
- 12. 12 C1 - Internal use FOCUS ON CUSTOMER FEEDBACK
- 13. 13 C1 - Internal use TECHACCELERATOR Sentiment Analysis Topic Enrichment Topic Extraction SEVERAL ALGORITHMS HELP ANALYZE FEEDBACK ‘‘ ’’ The mascara does not flake, but dries out too quickly. I prefer the old formula. Topic Extraction Topic Enrichment Sentiment Analysis Flakes Product dryness Discontinued formula Flakes Product dryness Discontinued formula
- 14. 14 C1 - Internal use FOCUS ON PRODUCTS
- 15. 15 C1 - Internal use TECHACCELERATOR THE GOAL OF PRODUCT DEFINITION IS TO GROUP TOGETHER PRODUCT COMING FROM DIFFERENT SOURCES Same platform Different platforms in the same country Platforms from different countries
- 16. 16 C1 - Internal use TECHACCELERATOR WE RECEIVE MULTIPLE DATA POINTS THAT CAN BE USED FOR MATCHING
- 17. 17 C1 - Internal use TECHACCELERATOR PRODUCT GROUPS ARE BASED ON A GRAPH WHERE EDGES ARE BUILT USING MULTIPLE DATA POINTS Edges based on UPC, Syndicated Reviews Edges based on UPC, Syndicated Reviews + Image and Title Similarity
- 18. 18 C1 - Internal use TECHACCELERATOR SIMILARITY PREDICTION IS OFTEN BASED ON TWO STEPS Get feature vector representation of the inputs Calculate distance between the vectors
- 19. 19 C1 - Internal use TECHACCELERATOR WE CHOSE SIAMESE NEURAL NETWORK ARCHITECTURE FOR TRAINING EMBEDDINGS Distance Base model Base model Architecture image source: https://omoindrot.github.io/triplet-loss INPUT A INPUT B
- 20. 20 C1 - Internal use TECHACCELERATOR WE TRAINED THE NETWORK USING TRIPLET LOSS Architecture image source: https://omoindrot.github.io/triplet-loss Base model Base model Base model INPUT A INPUT B INPUT C
- 21. 21 C1 - Internal use TECHACCELERATOR FIRST, WE APPLIED THE APPROACH TO IMAGES Architecture image source: https://omoindrot.github.io/triplet-loss Base model Base model Base model MobileNet2 / ResNet / InceptionV3 MobileNet2 / ResNet / InceptionV3 MobileNet2 / ResNet / InceptionV3 Cosine Similarity
- 22. 22 C1 - Internal use TECHACCELERATOR MODEL TRAINING SIGNIFICANTLY IMPROVED PERFORMANCE 1.0 precision Weights pre-trained on ImageNet ~0.60 recall Weights fine-tuned with Triplet Loss ~0.70 recall* +0.10 Base Model: ResNet50 *model didn’t reach 100% precison, but came very close to it (99.8%) 0.98 precision ~0.70 recall ~0.86 recall +0.16
- 23. 23 C1 - Internal use TECHACCELERATOR WE ALSO USED THE APPROACH WITH PRODUCT TITLES Architecture image source: https://omoindrot.github.io/triplet-loss Base model Base model Base model CountVectorizer with trainable weights CountVectorizer with trainable weights CountVectorizer with trainable weights Advanced Genifique Radiance Activating Serum 20ml/0.67oz Advanced Génifique Radiance Boosting Face Serum L’Oréal Paris Revitalift Face Serum 20ml Precision: 0.98 Recall: 0.68 Cosine Similarity
- 24. 24 C1 - Internal use TECH ACCELERATOR Join the team! theo.leccia@loreal.com audrey.castillo@loreal.com
Hinweis der Redaktion
- Each review is split into quotes Interesting points: Text classification model, one model per topic, allows for easier adjustment later if problems arise witha a specific category LDA, where prior is based on keyword – topic associations we already know about Trained to predict review rating based on text, custom,