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Essay: Product aspect ranking framework

Essay details:

  • Subject area(s): Engineering essays
  • Reading time: 3 minutes
  • Price: Free download
  • Published: August 11, 2017*
  • File format: Text
  • Words: 841 (approx)
  • Number of pages: 4 (approx)
  • Product aspect ranking framework
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Use of internet and e-commerce is growing very rapidly. Many products are available online. Most of the retail websites encourages consumers to write their feedbacks about products to express their opinions on various aspects of the products.
This gives rise to huge collection of reviews on web. These reviews contain rich

and valuable knowledge and may be an important resource for both consumers and

firms. Consumers always look for quality information from online reviews before

buying a product and firms can use these reviews as feedback for better quality product development, customer relationship management and for the development of new

marketing strategies.

A product may have large amount of aspects. Some of the product aspects are

more important than the others and have strong impact on the consumers decision

making as well as firms product development strategies. Identification of important

product aspects become necessary as both consumers and firms are benefited by this

technique. Consumers can easily make purchasing decision by paying attention to

the important aspects and firms can focus on improving the quality of these aspects

and thus enhance product reputation efficiently.

Simple solution for important aspect identification is frequency based approach

in which most frequently commented aspects in consumers reviews are selected as

important aspect. However, consumer’s opinions on the frequent aspects may not

influence their overall opinions on that product, and thus not influence consumer’s

purchase decisions.

Motivated by the above observations, this paper presents a product aspect ranking

system which can identify important product aspects from consumer reviews and

rank them by taking into account the frequency and consumers opinion on frequent



Use of internet and e-commerce is growing very rapidly. Numerous consumer

reviews of products are now available on the Internet. Consumer reviews contain rich

VPCOE,Baramati, Department of Computer Engineering 2015 12

and valuable knowledge for both firms and users. The reviews are often unordered,

leading to confusion in consumers mind about aspect on which they should give

importance. Consumers needs to know on which aspects they should purchase the


Hence, there is a need to develop an intelligent application which remove the

confusion in consumers mind and also helps firms ,to improve the product quality.

A product aspect ranking framework, automatically identifies the important aspects of products from online consumer reviews, aiming at improving the usability

of the numerous reviews.

The important aspect are identified based on two observations:

1. The important aspects are usually commented by a large number of consumers.

2. Consumer opinion on each aspects.


Consumers can conveniently make purchasing decision by paying more attentions

to the important aspects, while firms can focus on improving the quality of the aspect

and thus enhance product quality effectively. However, it is difficult for people to

manually identify the important aspects of products from numerous reviews. Therefore, an approach to automatically identify the important aspects is required.

Motivated by the above observations ,the system proposes a product aspect ranking framework which automatically recognizes the important aspects of product from

online consumer reviews, aiming at improving the usability of the numerous reviews.


1. Overview of Text Summarization Extractive Techniques[2]

Text summarization plays an important role in the area of natural language processing and text mining. Text summarization aims to create a compressed summary

VPCOE,Baramati, Department of Computer Engineering 2015 13

while retaining the main characteristics of the original set of documents. It uses linguistic methods to examine and interpret the text and then to find the new concepts

and expressions to best describe it by generating a new shorter text that conveys

the most important information from the original text document. In this paper, the

extractive Methods are used like Query based extractive text summarization, Multidocument extractive summarization.

A. Extractive Methods:

The approach naturally integrates linguistic features, such as part-of-speech and

surrounding contextual clues of words into automatic learning.

1.Term Frequency-Inverse Document Frequency (TF-IDF) Method:

In this method,to generate a generic summary, non stop-words that occur most frequently in the document(s) may be taken as the query words. Since these words

represent the them of the document, they generate generic summaries.

2.Cluster based method:

The document clustering becomes almost essential to generate a meaningful summary. Documents are represented using term frequency inverse document frequency

of scores of words. Term frequency used in this context is the average number of

occurrences (per document) over the cluster.


1. Extracted sentences usually tend to be longer than average. Due to this, part of

the segments that are not essential for summary also get included, consuming


2. Important or relevant information is usually spread across sentences, and extractive summaries cannot capture this (unless the summary is long enough to

hold all those sentences).

3. Conflicting information may not be presented accurately.

4. Pure extraction often leads to problems in overall coherence of the summary.

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