Project Synopsis on Opinion Mining

8/10/2019 Project Synopsis on Opinion Mining

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1. Introduction

In recent past, due to existence of numerous forums, discussion groups, and

blogs, individual users are participating more actively and are generating vast

amount of new data termed as user-generated contents. These new Web contentsinclude customer reviews and blogs that express opinions on products and services

which are collectively referred to as customer feedback data on the Web. As

customer feedback on the Web influences other customers decisions, these

feedbacks have become an important source of information for businesses to take

into account when developing marketing and product development plans. Recent

works have shown that the distribution of an overwhelming majority of reviews

posted in online markets is bimodal. Reviews are either allotted an extremely high

rating or an extremely low rating. In such situations, the average numerical star

rating assigned to a product may not convey a lot of information to a prospective

buyer. Instead, the reader has to read the actual reviews to examine which of the

positive and which of the negative aspect of the product are of interest. Several

sentiment analysis approaches have proposed to tackle this challenge up to some

extent. However, most of the classical sentiment analysis mapping the customer

reviews into binary classes positive or negative,and thus fails to identify the

product features liked or disliked by the customers.

2. Motivation

This project results from the need of extracting useful information from thelarge amount of unstructured and unorganized data available on the web. Because

of the explosion of data on the internet , there is a growing need to analyze this

unprocessed data and obtain meaningful information that can be used in other

applications.

There is a need to implement a system which can help consumers to directly get the

positive or negative opinion about the products without wasting time in reading the

reviews as stated by other users of those products. In this project, a framework has

been presented which first extracts the feature, modifier and opinion from thedataset and then using clustering mechanism divides them into discrete clusters on

the basis of users opinion, in which the intra-cluster similarity between the

features are high whereas the inter-cluster similarity is very low.


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3. Objective

1)To design and in feature based clustering techniques in sentiment analysis to

improve customer review summarization.

2)To process and analyze twitter or Facebook feeds to determine the responses and

feedbacks of the customers. Using sentiment analysis , we can determine the

content of the posts and how many customers have given positive or negative

reviews.

3)To use sentiment analysis and opinion mining to analyze customer reviews about

a specific product or service. We can determine how many users liked/disliked the

product/service, what are the strong and weak points of the product reviewed.

As an example , we can analyze the customer feedbacks about a smartphone. Usingsentiment analysis we can determine how many customers described the product

as good and how many disliked it. The positive features like battery , LCD display ,

RAM ,etc. that the users have rated high can be displayed in accordance with their

rankings. Similarly, the drawbacks of the product as d escribed by the customers

can be listed with their rankings.

4)To use opinion mining in improving the efficiency of web mining. Company

officials can directly analyze the general response and feedback of the customers

about their product or service without spending hours over reading the reviews

manually.

5) To implement a system which helps consumers to directly get the positive or

negative opinion about the products without wasting time in reading the reviews as

stated by other users of those products.


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4. Scope of the project

Fig. 1 presents the architectural details of the proposed opinion mining system,

which consists of five major modules Document Processor, Subjectivity/

Objectivity Analyzer, Document Parser, Feature and Opinion Learner, andReview

Summarizer and Visualizer. The working principles of these components are

explained in the following steps:

1) First step involves the collecting of review documents from various sources like e-

commerce websites such as Flipkart, amazon, etc. and social networking sites like

twitter,Facebook, etc.

2) In next step,Document Processor and Subjectivity/Objectivity Analyzer module is

employed, which consists of a Markup Language (ML) tag filter that divides an

unstructured web document into individual record-size chunks, cleans them byremoving ML tags, and presents them as individual unstructured record documents

for further processing.

3) ThenDocument Parser, and Feature and Opinion Learner module is

implemented. TheDocument Parser module uses Stanford parser, which assigns


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Documents

Project Synopsis on Opinion Mining