1 Team Members: Rohan Kothari Vaibhav Mehta Vinay Rambhia Hybrid Review System

2Introduction 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 System

Drawbacks 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.

Continued.. 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 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

Proposed Architecture

Continued.. 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.

Continued.. 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.

Continued.. 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. Continued..

Continued.. 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 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.

Continued.. 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.

15 Thank you!