1 Team Members: Rohan Kothari Vaibhav Mehta Vinay Rambhia Hybrid Review System.
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Transcript of 1 Team Members: Rohan Kothari Vaibhav Mehta Vinay Rambhia Hybrid Review System.
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IntroductionIntroduction
• Web contains a wealth of opinions about products which are expressed in newsgroup, posts, review sites, and elsewhere.
• Our project focuses only on reviews.
Product reviews on website such as Amazon and elsewhere often associate meta-data with each review indicating how positive (or negative) it is using a 5-star scale.
Existing Review SystemExisting Review System
Drawbacks of Existing SystemDrawbacks of Existing System
• However the reader’s taste may differ from the reviewers’.
• For example the potential buyer is looking for Image Stabilization feature or accelerometers in camera and often ends up with these results.
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• Hence the consumer is forced to wade through a large number of reviews looking for information about particular features of interest, which is time consuming.
Project OverviewProject Overview
• The proposed system tries to generate summary relating to the specific feature for a specific product and help the user compare two products side-by-side with respect to features.
• The system basically uses the two important concepts to provide a solution.• Opinion Mining• Text Summarization
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• KBS Generation• The input to the system will be knowledge base
consisting of the following lists:• Positive List: This is a database consisting of words
used to express positive opinion about a product or a topic.
Example: Cool, Great, Happy, Wow!!• Negative List: This database consists of words used to
express negative opinion about a product. Example: Bad, Awful, Terrible, Poor etc.
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• Application Programming Interface (API’s)• Amazon’s Product Advertising API was used to pull up
the following information from Amazon site. Product Information. Product Features. Product Reviews.
• Various forms (Variants) of the words used to describe features (Feature Variants) were generated using LiteMorph class available in Java.
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• The next stage after pulling up the reviews is “sentence fragmentation”.
• The sentences in the reviews would be fragmented using delimiters such as period, comma etc.
• The task of detecting the sentence boundary is difficult as marking the end of the sentence is often ambiguous.• For Example: A period “ . ” can be a decimal point.
Similarly sentences begin with a capital letter, but not all capitalized words start a sentence, even if they follow a period.
Opinion Mining Stage.• The fragmented reviews would then act as an input to
this stage. A sentence would be parsed and keywords relating to the features would be extracted based on the knowledge base.
• For Example: “The camera small in size. The optical zoom is good. The Image stabilization produces excellent images”.
• The output of this stage would be passed to the review stage.
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Review System• The rating of the feature of a particular product would
work on the following rules• Once the feature is detected, its neighboring words
would be checked for a negative or a positive opinion.
• If the opinion is found to be positive then it would be added as positive rating for that feature of the product.
• If the opinion is found to be negative then it would be added to negative rating for that feature of the product.
of the product.
Future ExpansionFuture Expansion
• Natural Language processing• Word Sense Disambiguation Many words have more than one meaning, we have
to select the meaning which makes the most sense in context.
• Example: “I am taking aspirin for my cold”. “Let's go inside, I'm cold”.• The word "cold" has several senses and may refer to
a disease, a temperature sensation, or a natural phenomenon.
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• Context dependent opinion words• For example, the word “small” can indicate a
positive or a negative opinion on a product feature depending on the feature.
• A more sophisticated sentiment analysis algorithm can be used to improve the opinion mining.
• The system can be build to compare two or more products at the same time based on the data source selected such as amazon.com, newegg.com and other consumer review sites.