The document presents a method for product feature ranking based on user reviews through sentiment analysis, which classifies opinions as positive, negative, or neutral. It outlines a systematic approach that includes aspect identification, sentiment classification, and ranking, enabling consumers to make informed purchasing decisions while helping firms enhance product quality. The proposed solution addresses challenges related to informal reviews and aims to improve aspect extraction efficiency.

1. INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY
VOLUME 4 ISSUE 1 – APRIL 2015 - ISSN: 2349 - 9303
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Product Feature Ranking Based On Product Reviews by Users
Saly Sakkaria
1
PG Scholar
MEA Engineering College,
Perinthalmanna, India
salysakkaria@gmail.com
Bineesh V
2
Assistant. Professor
MEA Engineering College,
Perinthalmanna, India
Bineeshv84@gmail.com
Abstract— Sentiment analysis or opinion mining is the process of determining the user view's or opinions explained in the form of
polarity (i.e. positive, negative or neutral) for a piece of text. This work introduces a method to extract features from the product
reviews, classify into positive, negative or neutral and rank aspects based on consumer's opinion. By aspect ranking, consumer's can
conveniently make a wise purchasing decisions by paying more attentions to the important aspects, while firms can focus on
improving the quality of aspects and thus enhance product reputation effectively.
Index Terms— Aspect identification, Aspect Ranking, Consumer review, Product aspects, Sentiment classification.
——————————  ——————————
1 INTRODUCTION
Sentiment analysis (also known as opinion mining) refers to the
use of natural language processing, text analysis and
computational linguistics to identify and extract subjective
information in the source materials. Sentiment analysis aims to
determine the attitude of a speaker or a writer with respect to
some topic or the overall contextual polarity of a document. The
attitude may be his or her judgment or evaluation. A basic task in
sentiment analysis is classifying the polarity of a given text at the
document, sentence, or feature/aspect levels, whether the
expressed opinion in a document, a sentence or an entity feature
or aspect is positive, negative, or neutral.
In the recent years the use of e-commerce is growning very
rapidly. Most retail Websites promote consumers to write their
feedbacks about products to express their opinions on various
aspects of the products. An aspect, which can also be called as
feature, refers to a component or an attribute of a certain product.
A sample review “The sound quality of Sony Experia is
amazing." reveals positive opinion on the aspect “sound quality"
of product Sony Experia.
Generally, a product may have a number of aspects. For
example, a Smart Phone has hundreds of aspects such as “screen
size," “camera," “memory size," ”sound quality.” one may say
that some aspects are more important than the others, and have
strong influence on the consumers’ decision making as well as
firms’ product development strategies. For example, some aspects
of Smart Phone e.g., “camera" and “memory size," are considered
important by most of the consumers, and are more important than
the others, such as “color" and “buttons." Hence, the
identification of important product aspects plays an essential role
in improving the usability of reviews which is beneficial to both
consumers and firms.
2 BACKGROUND
The process of product aspect ranking consisting of three main
Steps: (a) aspect identification; (b) sentiment classification on
aspects (c) Product aspect ranking. Given the consumer reviews
of a product, first identify the aspects in the reviews and then
analyze these reviews to find consumer opinions on the aspects
via a sentiment classifier and finally rank the product based on
importance of aspect by taking into account aspect frequency and
consumers’ opinions given to each aspect over their overall
opinions. Reviews can be posted on the webs in three different
types: Type (1) Pros and Cons: The reviewer is asked to describe
Pros and Cons separately. Type (2) - Pros, Cons and detailed
review: The reviewer is asked to describe Pros and Cons
separately and also write a detailed review. Type (3) - free format:
The reviewer can write freely, i.e., no separation of Pros and
Cons.
The aspect identification techniques used are Supervised
learning technique use the collection of labeled reviews to learn
an extraction model. This extraction model called as extractor is
then used for the identification of aspects in ne reviews. Most of
the supervised learning techniques are based on the sequential
learning. Various literatures show the different technique for the
learning of extractor. An Unsupervised learning method the
aspects are considered noun or noun phases and occurrence
frequency of noun and noun phrases is calculated. The frequent
noun or noun phrases are considered as aspects. The main
disadvantage of this method is that identified aspects candidates
may contain noise. Phrase dependency parsing takes the sentence
as input and segments it into phrases. Then these segments are
linked with direct arc. Phrase dependency parsing focuses on
phrases and not on a single word inside phrase. To make sure that
identified aspects candidate is to be an aspect language model
which is based on product reviews is used to predict scores of

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candidates. Model filter out low score candidates. Such model
may be biased to frequent terms in the review, and cannot sense
precisely the related scores of aspect terms as a result cannot
filter out noise efficiently.
Aspect sentiment classification technique uses Opinion words
for sentiment classification tasks. Desired states are express with
Positive opinion words while, undesired states are expressed with
negative opinion words. All opinion words, opinion phrases and
idioms are together called as the Lexicon. Three approaches used
to collect this opinion word list are manual, dictionary based and
corpus based approaches. The manual approach is very time
consuming and it is not used alone. It is usually combined with
the other two automated approaches to avoid mistakes that
resulted from automated methods. The two automated approaches
are presented in the following subsections. Holistic lexicon-based
approach improves the lexicon based method in by addressing
two issues that the opinion of sentiment words would be content
sensitive and conflict in the review. This method does not look at
the current sentence alone rather it uses the external information
and evidences in other sentence and other reviews. Some
linguistic conventions in natural language expression are used to
find the orientation of opinion word. This method required prior
domain knowledge or user inputs are needed. This approach is
highly effective when sentence contain multiple contradictory
opinion words.
3 LITERATURE SURVEY
Early work in sentiment analysis was primarily focused on
determining the semantic orientation of reviews. Among them,
some of the studies attempt to learn a positive or a negative
classifier at the document level. Lisette [1] propose a method for
extracting product aspect. Identification of aspect is based on
dependency analysis of customer reviews and use of opinion
words to select aspects on which customer has expressed their
opinions. This method gives good performance results and high
precision values. Here a Language Modeling Framework (LMF)
is used to rank aspects. In this paper a method of identifying
product aspects from customer reviews has been presented. First
candidate product aspects are identified taking in consideration
their grammatical structure. From this set, only those on which
customers has expressed their opinions are selected. The
proposed aspect filtering considers the dependency relations
between aspects and opinion words at three different levels of
relation. Finally the identified product aspects are ranked
according to their relevance.
Bing Liu focuses on customer reviews of products based on
opinion words. Here studied the problem of determining the
semantic orientations (positive, negative or neutral) of opinions
expressed on product features in reviews is discussed. Opinion
words are words that express desirable (e.g., great, amazing, etc.)
or undesirable (e.g., bad, poor, etc) states. In this a Feature Based
Summary (FBS) and OPINE is given to extract features and to
give score value. To overcome the problem of this, a method
called Opinion Observer [2] is proposed. It gives high F-measure,
precision and recall. Semantic orientation means, whether the
opinion is positive, negative or neutral. In holistic lexicon-based
approach , instead of looking at the current sentence alone, this
approach exploits external information and evidences in other
sentences and other reviews, and some linguistic conventions in
natural language expressions infer orientations of opinion words.
No prior domain knowledge or user inputs are needed.
S.Nandhinis [4]paper consists of product ranking based on
features identified by the consumer reviews. The methods used
are feature identification of product, classification of features and
the probability ranking algorithm to find the overall rating of
product[3]. The purpose of the work are used to identify the
important features of the consumer reviews based on the product
they purchased and used through online shopping. This paper
consists the product ranking depends upon the features identified
in consumer reviews. In a given customer reviews, important
features are identified using a shallow dependency parser, and
classified them into positive or negatives via sentiment
classification using NLP, finally apply the ranking algorithm to
determine the particular product ratings.
Another work in aspect extraction by Neha S. Joshi's [5] paper
presents sentiment analysis approaches and the types of
algorithms used for supervised, unsupervised and semi
supervised methods. These related works help to analyze
sentiment analysis, in-depth and to familiarize with other works
done on the subject. The analysis levels can be done at three
levels namely document level, sentence level and Feature level
analysis. Supervised machine learning techniques has shown
better performance than unsupervised machine, earning
techniques. However, the unsupervised methods is important too,
because supervised methods demand large amounts of labelled
training data that are very expensive whereas acquisition of
unlabeled data is easy.
Lei Zhang, [6] the authors have used aspect based opinion mining
, which determines the aspect of given reviews and classify the
reviews for each feature. Here opinion mining is based on an
unsupervised technique using data collection, POS tagging,
feature extraction, seed list preparation, polarity detection and
classification. Output is the product with high accuracy. Aspect
based opinion mining is one of the level of Opinion mining that
determines the aspect of the given reviews and classify the
review for each feature. In this paper an aspect based opinion
mining system is proposed to classify the reviews as positive,
negative and neutral for each feature. Negation is also handled in
the proposed system. In this paper an Aspect based Opinion
Mining system named as “Aspect based Sentiment Orientation
System” is proposed which extracts the feature and opinions from
sentences and determines whether the given sentences are
positive, negative or neutral for each feature. Negation is also
handled by the system. To determine the semantic orientation of
the sentences a dictionary based technique of an unsupervised
approach is adopted. To determine the opinion words and their
synonyms and antonyms WordNet is used as a dictionary. The
proposed system is based on unsupervised technique. The
dictionary based approach of an unsupervised technique is used

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to determine the orientation of sentences. WordNet is used as a
dictionary to determine the opinion words and their synonyms
and antonyms. The objective of this paper is to determine the
polarity of the customer reviews of mobile phones at aspect level.
System performs the aspect based opinion mining on the given
reviews and the feature wise summarized results generated by the
system will be helpful for the user in taking the decision.
4 OBSERVATION ANDANALYSIS
TABLE I show several techniques for product aspect ranking
Method
Word
Dependency
Lexicon
Based
Approach
Product
Ranking
Feature Level
Sentiment
Analysis
Aspect
Identificatio
n
Customer’s
like and
dislike
Semantic
operations
Positive
and
negative
classifier
Positive and
negative
feedback
Aspect
Ranking
By Overall
rating
By Opinion
Orientation
By
weightage
By overall
rating
Evaluation
of aspect
By baseline
method
By Feature
based
summary
By N-
Gram
Model
By N-Gram
Model
The above table show how products are ranked in different
papers. For ranking, first it identifies the product aspects and
based on this, it identifies the pros and cons of each product and
rank it based on the reviews by the user. Evaluation of aspects is
done based on the N Gram model , parts of speech tagger and by
baseline method.
5 PROBLEM STATEMENT
People keep on commenting reviews. This causes hanging of
sites for various consumers. To identify features is time
consuming and is not easy to do also. Also identified aspects
contains noises. This is because these reviews are usually short,
informal texts written by non-experts and frequently contain
mistakes in spelling, grammar, use of non dictionary words such
as abbreviations or acronyms of common terms, mistakes in
punctuation, incorrect capitalization etc. Identifying aspects in
the free text reviews is difficult. Because a lot of customers give
their opinion as free text review. Free text reviews are written in
paragraphs. So to extract aspects from it is difficult and time
consuming. So online product reviews still need to improve
because all are using online reviews in large nowadays. To
overcome these problems I proposes a Language Modeling
Framework to extract aspects and rank these aspects in
hierarchical order.
6 PROPOSED WORK
This paper is organized as follows: In Section A, the architecture
is illustrated. In Section B, the module description.
A. System Architecture
Figure 1 System Architecture
B. Module Description
This project consist of mainly five modules. They are the Admin
module, Aspect Identification Module, Opinion Extraction
Module, Consumer module and Ranking module.In the Admin
module, Admin will create all types of product Categories. In
these Product categories we will deal with only the electronic
items like Mobile Phones, Laptops, and Cameras. Besides the
Admin will upload all the type of products based on categories
respectively. In that we divide product into product aspects to
store and retrieve in the server.
In aspect identification module, Users or consumers will find the
various aspects of a particular product. For example (A product
(i.e. Samsung Laptop) contains various aspects that are,
processor, Brand, RAM Speed, HARDDISK, Battery, Color, and
Features).
In opinion extraction module to admin will collect all the reviews
of each product of a particular product type present in the server.
In that he/ she will generate an opinion extraction based on pros
and cons. Like how many users or consumers are reviewed by a
product and how much free text are given in.
In consumer module, only registered users can access this phase.
The users can be given ratings and review of any product. Or
search the product categories in review phase user can see the 10
performance oriented phrase of each product in the server in that
we will give the performance ratings to the admin and he/she will
calculate the average performance.
In ranking module admin will generate the ranking or products
aspects of each particular product present in our server, based on

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user reviews generated by the above phase admin created the
ranking of each product. So all the users benefited for searching,
which product is best in the market and which one to buy.
7 RESULT
FIGURE 2 OUTPUT
CONCLUSION AND FUTURE WORK
This paper presented an overview on the product aspect ranking
techniques to identify important aspects of products. Product
aspect ranking process contains three main steps, i.e. product
aspect identification, aspect sentiment classification and aspect
ranking. First the pros and cons of aspects are identified. Then
developed a probabilistic aspect ranking algorithm to find the
various aspects of products from numerous reviews. The product
aspects are finally ranked according to their importance scores.
Future work for this project is using a graph based ranking
method. In this, the sentence can be represented in a graph, where
each node corresponds to a sentence and each edge characterizes
the relation between two sentences.
ACKNOWLEDGEMENT
An endeavor over a long period may be successful only with
advice and guidance of many well wishers. We take this
opportunity to express our gratitude to all who encouraged us to
complete this work. We would like to express our deep sense of
gratitude to our respected Principal for his inspiration and for
creating an atmosphere in the college to do the work. We would
also like to thank Head of the department, Computer Science and
Engineering.
REFERENCES
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“Extraction and ranking of product aspects based on word
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[3] X. Ding, B. Liu, and P. S. Yu, “A holistic lexicon-based approach
to opinion mining," in Proceedings of the 2008 International
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[4] M. Popescu and O. Etzioni, “Extracting product features and
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[5] R.Sharma, S.Nigam, and R.Jain, “Mining of product reviews at
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