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Review Summary System

Review Summary System

Project done as part of course work.

dharmeshkakadia

November 25, 2011
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  1. Team – 22
    M Manoj Kumar – Srinath Ravichandran - Dharmesh kakadia – Sandhya S
    (201107502) - (201107625) - (201107616) - (201107617)
    REVIEW SUMMARY SYSTEM

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  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

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  3. OVERALL WORK FLOW
    Feature
    Extraction
    Sentiment Analysis
    Sentiment
    Classification

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  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.

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  5. Review •  Raw Review
    Sentence
    Pruning •  Preprocess data
    •  List of valid features
    Dependency
    relations
    •  Using Stanford
    Parser
    Opinions>
    Semantic
    Analyzer

    NoSQL
    (mongo)
    Feature
    DB
    •  Each sentence is
    passed through NLP
    logic.
    •  Features are
    extracted and rated
    according to the
    opinion of the
    setence.

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  6. PARALLELIZING WITH HADOOP
    Mapper
    Reducer
    Reducer
    Reducer
    mobile2
    Summary Data Base
    mobille1 mobille2 mobille3
    (Tag the Review)

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  7. DATABASE SCHEMA
    Trained Data
    •  Nouns #
    •  Modifiers #
    Tagged Reviews
    •  Features
    •  Ratings
    •  Review Text
    Review
    Summary
    •  Features
    •  Average
    Rating
    Product X

    View Slide

  8. RESTFUL WEB SERVICES
    •  System exposes results as restful web services.
    Review
    System

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  9. EXPERIENCES & LEARNING
    •  NLP Dependency Relationships!!!
    •  REST is BEST
    •  SCHEMA defines EVERYTHING!!

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  10. FUTURE WORK
    •  Better feature Extraction.
    •  Synonym match can be extended with Wordnet::Similarity.
    •  Can be further optimized for blazing performance.
    •  Preprocess user query.

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  11. TOOLS USED
    •  NLP
    •  Stanford Parser
    •  Wordnet (Synonyms)
    •  Sentiwordnet
    •  Hadoop 20.2
    •  Mongo DB

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