Review Summary System

Project done as part of course work.

Transcript

1. Team – 22 M Manoj Kumar – Srinath Ravichandran - Dharmesh kakadia – Sandhya S (201107502) - (201107625) - (201107616) - (201107617) REVIEW SUMMARY SYSTEM
2. OVERVIEW •  System to summarize reviews from various sources •  Users can view and compare products based on features •  Results exposed as RESTful web-service •  Ability to cater to different products
3. OVERALL WORK FLOW Feature Extraction Sentiment Analysis Sentiment Classification
4. DETAILED FLOW CHART Reviews Parse and Tag Feature Extraction Feature DB Opinion DB •  Once for a category of product •  Nouns #frequency •  Adjectives #frequency •  Classifier is designed based on this data.
5. Review •  Raw Review Sentence Pruning •  Preprocess data <features> •  List of valid features Dependency relations •  Using Stanford Parser <Feature Opinions> Semantic Analyzer <Ratings> NoSQL (mongo) Feature DB •  Each sentence is passed through NLP logic. •  Features are extracted and rated according to the opinion of the setence.
6. PARALLELIZING WITH HADOOP Mapper Reducer Reducer Reducer mobile2 Summary Data Base mobille1 mobille2 mobille3 (Tag the Review)
7. DATABASE SCHEMA Trained Data •  Nouns # •  Modifiers # Tagged Reviews •  Features •  Ratings •  Review Text Review Summary •  Features •  Average Rating Product X
8. RESTFUL WEB SERVICES •  System exposes results as restful web services. Review System
9. EXPERIENCES & LEARNING •  NLP Dependency Relationships!!! •  REST is BEST •  SCHEMA defines EVERYTHING!!
10. FUTURE WORK •  Better feature Extraction. •  Synonym match can be extended with Wordnet::Similarity. •  Can be further optimized for blazing performance. •  Preprocess user query.
11. TOOLS USED •  NLP •  Stanford Parser •  Wordnet (Synonyms) •  Sentiwordnet •  Hadoop 20.2 •  Mongo DB