SUMMARIZATION AND OPINION DETECTION OF PRODUCT REVIEWS

Mentor : Aditya Joshi

By Group Number-55 Project Number-17

College : IIIT-Hyderabad

Team Members:

Karan Dhamele (201101182)

Anusha Eagalapati(201305627)

Lokesh Mittal (201305650)

Contents

Introduction Dataset Approach Block Diagram Evaluation Screenshot Future Scope Conclusion Reference

Introduction

With the rapid expansion of e-commerce, the number of reviews that a product receives grows rapidly. This makes it hard for a potential customer to read them to make an informed decision on whether to purchase the product. So in this project we are generating feature based summaries of customer reviews of products.

Dataset:

Taken so many product links from FLIPKART and Extracted reviews from those products.

Approach

Scrapping: Using Jsoup we are parsing the html page and getting reviews out of it.

Feature Extraction:Using Stanford Dependency Parser features are being extracted.

Opinion Identification for review: 1)Opinion Word Extraction: A set of adjective words is

identified using a natural language processing method2) Orientation Identification for Opinion Words: For each opinion word, we need to identify its semantic orientation, which will be used to predict the polarity (positive or negative) of each opinion sentence. For this we used the SentiWordNet to know the orientation of the opinion words.

Approach (continue…)

3) Predicting the Orientations of Opinion Sentences: Now after getting the orientation of the individual opinion words we predict the orientation of the whole review sentence for the particular feature whether the review is positive or negative for that feature.

Summary:Generation the final feature-based review summary involves following steps:1.For each feature, related opinion sentences are put into positive and negative categories according to the opinion sentences’ orientations. A count is computed to show how many reviews give positive/negative opinions to the feature. 2.All features are ranked according to the frequency of their appearances in the reviews.

BLOCK DIAGRAM

Crawl Reviews using Jsoup

ReviewData-base

Dependency Parsing using Stanford Dependency Parser

Features Identification

Frequent Features

Opinion Word Extraction

Orientation of Opinion WordUsing SentiWordNet

Opinion Words

Orientation of Review Sentence UsingSentiWordNet

Summary Generation And Graph Generation Using Chart.js

Evaluation and Results

The type for the evaluation of our tool will be manual ie we compared our results manually with flipkart results. We will evaluate this summarization in the following three perspectives:

The accuracy of product feature extracted. The accuracy of the opinion sentence

extraction. The accuracy of the opinion prediction for

that sentence

Screenshot

Future Work

In our future work, we plan to further improve and refine our techniques, and to deal with the outstanding problems identified above, i.e., pronoun resolution, determining the strength of opinions, and investigating opinions expressed with adverbs, verbs and nouns. We also plan to take care of implicit features of the product.

Conclusion

Objective of our project is to provide a feature-based summary of product. By doing this way we can solve above problem through which we are providing a better and easy way for online customer to decide whether to purchase the product or not.

References

https://www.ideals.illinois.edu/bitstream/handle/2142/18702/survey_opinionSummarization.pdf?sequence=2

http://gate.ac.uk/sale/eswc11/opinionmining.pdf

http://www.cs.uic.edu/~liub/publications/kdd04-revSummary.pdf

http://www.seas.upenn.edu/~cse400/CSE400_2009_2010/final_report/Schaye_Feczko.pdf