Trust is an important factor for E-commerce and has been an issue for the peopleusing E-Commerce for their habitual shopping purpose, whereas E-Commerce is verymuch money saving as compared to traditional offline shopping. But peoplefear using E-Commerce just because there is no contingence with the seller. So it is a hugechallenge to isolate this fear from the mindset of buyers. The only alternative to isolatethis fear is coming up with a reliable trust in their minds. There are many conditions which should be taken care of depending upon which our trustwhich includes past history, reputation, websitequality, and seller's response and customer support.

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International Journal of Computer Engineering & Technology (IJCET)
Volume 8, Issue 2, March-April 2017, pp. 06–13, Article ID: IJCET_08_02_002
Available online at
http://www.iaeme.com/IJCET/issues.asp?JType=IJCET&VType=8&IType=2
Journal Impact Factor (2016): 9.3590(Calculated by GISI) www.jifactor.com
ISSN Print: 0976-6367 and ISSN Online: 0976–6375
© IAEME Publication
PRODUCT REPUTATION AND GLOBAL
RATING IN E-COMMERCE
Arun Rajput, Archit Raut, Ritesh Salian
Student, Dept. of CSE,
St. John College of Engineering and Management, Palghar, Maharashtra State
Sushant Patil
Assistant Professor, Dept. of CSE,
St. John College of Engineering and Management, Palghar, Maharashtra State
ABSTRACT
Trust is an important factor for E-commerce and has been an issue for the people
using E-Commerce for their habitual shopping purpose, whereas E-Commerce is very
much money saving as compared to traditional offline shopping. But people fear using
E-Commerce just because there is no contingence with the seller. So it is a huge
challenge to isolate this fear from the mindset of buyers. The only alternative to isolate
this fear is coming up with a reliable trust in their minds. There are many conditions
which should be taken care of depending upon which our trust reputation system would
be build. They are widely classified which includes past history, reputation, website
quality, and seller's response and customer support.
Key words: Trust, Feedback, Rating
Cite this Article: Product Reputation and Global Rating In E-Commerce, Arun Rajput,
Archit Raut, Ritesh Salian, Sushant Patil. International Journal of Computer
Engineering & Technology, 8(2), 2017, pp. 06–13.
http://www.iaeme.com/IJCET/issues.asp?JType=IJCET&VType=8&IType=2
1. INTRODUCTION
Proposed System aims at creating trust and proclaiming it in online communities while giving
actionable results. Those results such as trust unit, scores and Sentiment Analysis help users to
make a decision about purchasing a particular product. Proposed design will use both ratings
and especially semantic feedbacks in order to calculate trust weight and to classify comments
and users. Sentiment Analysis will be carried out on the user's feedbacks. Feedbacks of the
users will be submitted only if the threshold value of the feedback is above certain baseline. We
have focused on the reputation to heighten the quality of the websites. In the implementation
part we make sure that the feedback given by the user is legit. Sentiment Analysis is used to
know the sentiment and emotions behind the given feedback and check its rapport with rating
given by the same user. If rapport is adequate user has to like or dislike prefabricated feedbacks.

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Using both the rapport and like- dislike results, Trust Degree of the user is calculated. If the
Trust Degree is above certain threshold then the feedback of that user is submitted to our
website else the user is blocked. This is how we improve website quality by our implementation.
In contrast to such papers, our main contribution in this work is to analyze the attitude adopted
by the user toward specific prefabricated textual feedbacks. Our proposed design aims to
provide the user with the chances to like or dislike – via a specific interface- some feedbacks
summarizing several former users feedbacks in addition to fake and prefabricated feedbacks.
This selection takes place after that this user gives his appreciation (a numeric value) on the
product within his textual feedback. Then the user is asked to validate his appreciation and
feedback.
We have used the concepts of Natural Language Processing like tokenization [1], word
normalization [2], and bag-of-word model [3]. All this is to classify feedbacks as per their actual
meaning and sentiment of the user is known.
Our approach relies on an algorithm that includes semantic feedback analysis in order to
generate most trustful reputation score for a product since feedbacks affect user’s decisions
more than numeric scores alone. This proposed algorithm calculates and updates also the trust
degree of the user after any participation in the TRS.
2. LITERATURE SURVEY
2.1. Trust Network Analysis with Subjective Logic
The network of trust contains the transitivity and the relationships between organizations,
agents which are afflicting to software and the people which are connected through the medium
of interactions and relationships [4]. By formulating the relationships of the trust, for example
a reputation scores or a subjective measures of trust between parties within the community can
be derived by analyzing the trust paths linking the parties together. This article describes a
method for trust network analysis using subjective logic (TNA-SL). It provides a simple
notation for expressing transitive trust relationships. The main contribution of this paper is a
method for discovering trust networks between specific parties, and a practical method for
deriving measures of trust from such networks. A method for simplifying complex trust
networks so that they can be expressed in a concise form and be computationally analyzed.
Trust measures are expressed as beliefs, and subjective logic is used to calculate trust between
arbitrary parties in the network. Monitoring the behavior of peers in P2P communities.
Assessing the reliability of peers in Internet communities. Monitoring the behavior of peers in
P2P communities. Assessing the reliability of peers in Internet communities.
Figure 1. Trust Transitive principle

3. Arun Rajput, Archit Raut, Ritesh Salian, Sushant Patil
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2.2. Jason: A Scalable Reputation System for the Semantic Web
In this paper [5] it propose the enhancement of Web 2.0 by a scalable and secure cross-platform
reputation system that takes into account a user’s social network. The proposed solution Jason
is based on standard methods of the semantic web and does not need a central entity. It allows
quick and modifiable evaluation of arbitrary content on the World Wide Web. In other
reputation systems it provides instrumentation to ensure the authenticity of web content, thus,
enabling the user to explicitly choose information published by trusted authors. Confidentiality
of ratings is been maintained Anonymity of users is not maintained. Integrity of web content
and ratings are not maintained. By this, the security in the system is not maintained. In contrast
to many papers this paper does not propose an algorithm for reputation neither propose a certain
system or set of rules that define a specific set of possible reputations or ratings. Instead, it
allows each user to assign his/her own meaning to a certain rating and spread this assumptions
within his social network. Every user calculating this rating has two options: he creates his own
based on his experience with the rater or to use the assumption of the rating presented by the
social network. After their creation reputation and ratings have to be stored somewhere. The
allocation of reputation and ratings might either be distributed on user devices in the reputation
network, centrally allocated at specific reputation servers or decent rally allocated with the
content itself. All reputation stored can only be calculated by a user of the reputation system if
there is a flow of information in the reputation network towards him.
2.3. A New Reputation Algorithm for Evaluating Trustworthiness in E-
Commerce Context
Robust Trust Reputation Systems (TRS) provide litigable information to support relying parties
taking the right decision in any electronic transaction. In fact, as security providers in e-services,
TRS have to allegiant calculate the most trustworthy score for a targeted product or service.
Thus, TRS must rely on a robust architecture and suitable algorithms that are able to select,
store, generate and classify scores and feedbacks. In this work, the paper propose a new
architecture for TRS in e-commerce application which includes feedbacks’ analysis in its
treatment of scores. In fact, this architecture is based on an intelligent layer that submit to each
user who has already given his recommendation, a collection of prefabricated feedbacks
summarizing other users’ textual feedbacks. Use of prefabricated feedback. Two trust degree
and the user appreciation about the product the global reputation score is calculated. Text
mining algorithm was not defined. Contradictory feedbacks were not taken care of (actions)
[6].

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Figure 2. Pseudo-code for the trust degree of the user
The approach depends on an algorithm that comprises of semantic feedback analysis to
create most trustworthy reputation score for a product since feedbacks affect user’s decisions
more than numeric scores alone. This proposed algorithm calculates and updates also the trust
degree of the user after any participation in the TRS. In this approach, each user who wants to
leave the rating (appreciation) and a textual feedback viz. semantic feedback, we analyze the
intervention using this algorithm. After verification, the user recommendation will have reached
by any other user and then it is a recommendation for everyone interested in the product or even
not. Then, system is supposing that system have a path relaying every user. The most important
is to analyses at any intervention the user’s attitude in order to deduce the user’s intention
concerning the rating of that specific product.
3. PROPOSAL METHODOLOGY
3.1. Naïve Bayes classifier as sentiment analyser
We want to determine and predict whether a review is negative or positive given only the text.
In order to do this, we’ll train an algorithm using the user’s review on product (feedback) and
a classifier file, and then make predictions on the reviews. For our classification algorithm,
we’re going to use naive bayes. A naive bayes classifier works by figuring out the probability
of different attributes of the text being associated with a certain class i.e. positive, negative or
neutral. This is based on bayes’ theorem. The theorem is P(A|B)=[P(B|A)*P(A)]/P(B). This
basically states the resultant probability of A given that B is true equals the probability of B
given that A is true times the probability of A being true, divided by the probability of B being
true [7]

5. Arun Rajput, Archit Raut, Ritesh Salian, Sushant Patil
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Figure 3. System Architecture Design
Naive Bayes Classifiers is used for Sentiment Analysis. Our purpose is to determine the
subjective value of a text-document, i.e. how positive or negative is the content of a text
document.For example a statemnt with positive words like good, best, amazing, etc would be
classified as positive while statement with negative words like bad, worst, gross, awful, etc
would be classified under negative class. Use of classifiers as sentiment analysis seems quite
easy and promising at first glance but unfortunately, for this purpose these Classifiers fail to
achieve the accuracy. This failure is due to the subtle essence of human language; sarcasm,
irony, context interpretation, use of slang, cultural differences and the different ways in which
opinion can be expressed like subjective vs comparative, explicit vs implicit.
3.2. Tokenization
Tokenization is the process of chopping a stream of text up into words, phrases, symbols, or
other meaningful elements called tokens. The list of tokens becomes input for further processing
such as parsing or text mining. Tokenization is used both in linguistics where it is a form of text
segmentation, and in computer science, where it forms part of lexical analysis [1].
3.3. Word Normalization
Word normalization is the process of transforming text into a single canonical form that it might
not have had before. Normalizing text before storing or processing it allows for separation of
concerns, since input is guaranteed to be consistent before operations are performed on it. Text
normalization requires being aware of what type of text is to be normalized and how it is to be
processed afterwards [2].
3.4. Bag-of-words
After the text has been segmented into sentences, each sentence has been segmented into words,
the words have been tokenized and normalized, and we can make a simple bag-of-words model
of the text. In this bag-of-words representation we only take individual words into consideration
and give each word a specific subjectivity score. This subjectivity score can be looked up in a
sentiment lexicon [8]. If the total score is positive then the text will be classified as positive and
if it’s negative then text will be classified as negative.
In this model, a text (such as a sentence or a document) is represented as the bag (multiset)
of its words, disregarding grammar and even word order but keeping multiplicity [3].

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We have used Tokenization, Word Normalization, and Bag-of-words model in filtering and
classification of textual feedbacks.
Feedback is classified as positive feedback or a negative feedback and corresponding
subjective score is generated for those feedback. This subjective score of feedback is used for
calculating the trust degree of user.
3.5. Trust Reputation System
The user first starts by giving an appreciation (rating) and a textual feedback about particular
product. When user clicks on submit button to validate the given information, were going to
redirect the user to another interface displaying this message for example: "please give us
your opinion about the following feedbacks:"
Figure 4 TRS Flowchart

7. Arun Rajput, Archit Raut, Ritesh Salian, Sushant Patil
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In this we will find selected feedbacks from the database from different types. Those
feedbacks can be fabricated for summarization of numerous users feedbacks collected in the
database. The produced feedbacks can be stored in another knowledge base. So as much as we
add new feedbacks in the ordinary data base, we will observe that the knowledge data base with
prefabricated feedbacks with text mining algorithms and tools. Wherein, some users can give
already summarized feedbacks that can directly be added in the knowledge data base.
Actually, before the feedback of the user is send and appreciation about the product to the
trust reputation system, we have to verify the rapport between them so that contradictious
feedback will be avoided. In the redirected interface, we will display several feedbacks from
different types. However, the user can specify the number of feedbacks to be liked or disliked.
Obviously, we can also specify the lowest and the highest number of feedbacks to be displayed
by the user.
We are aiming through redirecting detect and analyzing the user's purpose behind his
intervention on the ecommerce application. Henceforth, we examine and evaluate his purpose
using pre-fabricated feedbacks with different types accordingly, we use our reputation
algorithm studied in order to generate the user trust degree which plays a vital role to rectify
his appreciation according to his trust degree and generates the score of the feedback.
Obviously, each feedback has trustworthiness in a threshold [-5, 5]. The nearest is the
trustworthiness to 5, the most trustworthy the feedback is. The nearest is the trustworthiness to
-5, the very untrustworthy is the feedback. If the feedback is reliable and trustworthy then its
score would be included in [0, 5] range else it would be included in [-5, 0] range.
Later, we have to generate the Global Trust Reputation (GTR) score of the product using
the users rating and his trust degree. In fact, a possible example for such a rating method might
be school marks and coefficients. In fact, at university, when a course is important for a certain
field, its coefficient would be great and then the effect of its mark would be greater. In the same
way, we consider the trust degree of the user as a coefficient and his appreciation as a mark.
Accordingly, to calculate the global trust score of the product, we sum all the appreciation
values multiplied by their respective coefficient and later dividing its result of the summation
on the summation of all coefficients.
4. CONCLUSION AND FUTURE WORK
Semantic feedbacks are of more significance than just single scores [9].Understanding the
intention behind the users review unlocks more potential to avoid and discard spam reviews.
By using naïve bayes we have analyzed the sentiments of the feedbacks given by various users.
Also the user is asked to provide rating and opinion on prefabricated feedback in terms of like
and dislike. Combining these multiple factor, sentiments behind feedback, rating and the
affinity of the user is toward the product is checked. We in our work have considered only few
of the many factors. In future work we will work on Multiple Factor Analysis [10] that create
online environment reliable and dependable for users. We also plan to include and implement
concept of expert system [11], where a group of experts would be ranked based on their review
and rating of product and users could express their opinion on those expert reviews.

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REFERENCES
[1] https://en.wikipedia.org/wiki/Tokenization_(lexical_analysis)
[2] https://en.wikipedia.org/wiki/Text_normalization
[3] https://en.wikipedia.org/wiki/Bag-of-words_model
[4] “Trust Network Analysis with Subjective Logic” - Audun Jøsang Ross Hayward Simon
Pope
[5] Jason: A Scalable Reputation System for the Semantic Web Sandra Steinbrecher, Stephan
Groß, and Markus Meichau
[6] A New Reputation Algorithm for Evaluating Trustworthiness in E-Commerce Context
Hasnae RAHIMI, Hanan EL BAKKALI
[7] https://www.dataquest.io/blog/naive-bayes-tutorial/
[8] http://sentiment.christopherpotts.net/lexicons.html
[9] Baccianella, S., Esuli, A. & Sebastiani, F. New Gener. Comput. (2013) 31: 47.
doi:10.1007/s00354-012-0122-y.
[10] Dr. Ramesh Sardar, Fdi in e-commerce: pros & cons, International Journal of Management,
5(2), 2016, pp. 49–53.
[11] Sijoy Syriac and Dr. Raghuram J, The Rise of E-Commerce and its Subsequent Impact on
the Indian Retail Market. International Journal of Management, 7(7), 2016, pp. 271–275
[12] Gupta D, Ekbal A. "Determing Trustworthiness in E-Commerce Customer Reviews".
[13] S. Patil and N. Ansari, "User interaction using ExpertTop analysis," 2014 International
Conference on Advances in Communication and Computing Technologies (ICACACT
2014), Mumbai, 2014, pp. 1-6.