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ERASMUS MUNDUS cLINK
PROGRESS REPORT
Date: 08/12/2013
Title: Product Reputation Evaluation based on Social Network
using Multi Agent System
Name of Student: Umar Farooq
x
PhD
Post Doc
Abstract:
The Word Wide Web has dramatically changed the way people express their opinion about
product and services. The customer can post product reviews on different websites and
express their emotion in internet discussion forums, groups, blogs, twitter etc. There is a rich
source of information available on Internet about products and services which can be used by
both customer as well as industrial organization to make decisions. It is a challengeable task
to collect and organize these information, therefore most of the industrial organization ignore
it. Organizations currently make decision throughout product life cycle using Product Life
Cycle Management System (PLM). We want to incorporate the unstructured information
available on social network and to integrate it with structure information in PLM to build a
shared vision. This shared knowledge can be used to make useful decision throughout product
life cycle. The aim of the thesis is to gather, process and analyze knowledge from web and
combined it with PLM. We are proposing a multi agent system in which each agent works on
different type of knowledge. Each agent will share knowledge with other in order to build
comprehensive knowledge and this knowledge can be used by organization to make some
useful decisions.
Keywords : Product Reputation, Sentiment Analysis, Opinion Mining, Multi Agent System
In order to proposed a multi agent system for product reputation evaluation I will work on the
following tasks.
1)
2)
3)
4)
5)
6)
Information extraction from Social Network
Data Cleaning
Sentiment Analysis (I am currently working on this topic)
Reputation Evaluation
Integration of unstructured data with PLM ( Product Knowledge Management)
Industrial Case Study
Schedule for 24 Months
Month
April, 2013
May, 2013
June, 2013
July, 2013
August, 2013
September, 2013
Topic, Description and Outcomes
Topic: Sentiment Analysis
Topic Description:
In the first 6 months I will work on Sentiment analysis, as I am already
working on it, so I will continue it.
I will try to improve the architecture that I have already been proposed
for sentiment analysis; I will try to automate it. After automation we will
be able to perform experiment to produce result and to check the
accuracy of our system.
First I will check the system on product reviews and then on tweeter
data and after than on other social networks.
During this duration I will also work on Feature base sentiment analysis,
so that we can find the opinion of customer about features of product.
Note: Sentiment Analysis for product and the proposed architecture is
explained in detail in the last section of this document.
Outcomes:
One research Publications in Journal on Sentiment Analysis
Month
October, 2013
November,213
December, 2013
Topic, Description and Outcomes
Topic: Information extraction from SN
Topic Description:
This work will answer that how we can extract data about a product
from SN? On the basis of which criteria data will be extracted?
This work will give a review of existing methods and try to proposed new
and efficient methods for information extraction.
We will propose agent architecture which will extract data from different
social networks sites.
We will propose an opinion search engine which will take the product
from user as in input, and will extract opinionated text from different
social networks.
This work will address the following research questions.
January, 2014
February, 2014
March, 2014
April, 2014
May, 2014
June, 2014
1. How to extract reviews of product from different review sites
(such as amazon.com, eopinion.com etc)?
2. How to extract data from tweeter about product?
3. How to extract data from blogs and forums?
4. How to extract data from other SNs?
Outcomes:
One or two research publications on information extraction from SN
Topic: Cleaning of user Data
Topic Description:
As we know that user comments have a lot of noise, so we need to
remove the noise before performing sentiment analysis. We will use the
existing methods to remove noise. Which spelling correction method
can be used?
Reviews have little noise however discussion forums, blogs and tweeter
have a large amount of noise.
In tweeter the users use special characters and symbols to express
opinion which is called Photonics. We will use the existing methods in
order to convert the photonics into text expression?
Topic: Reputation Evaluation
Topic Description:
In this work I will study the current reputation model use for product
evaluation and will try to propose a model.
Most current product reputation model are for customers, therefore we
need to propose a reputation model for organization, so that they can
know about their product strengths and weaknesses.
Some reviews sites uses 5 star to rank the product, we also incorporate
those model and will also try to provide a visualization.
This work will address that on which criteria we can evaluate the product
reputation?
We will propose a multi criteria model per source and then will integrate
to evaluate production reputation which will be based on multi sources
(Many SNs) and multi criteria.
In this way we will be able to evaluate product reputation which is based
on unstructured date in the form of social networks.
Outcomes:
One Publication on Product Reputation Evaluation Model
Month
Topic, Description and Outcomes
July, 2014
Topic: Integration of unstructured data with PLM System
August, 2014
Topic Description:
This work will address that how we can integrate the knowledge that we
have learnt from SN with Product Life Cycle Management System.
In another words how we can integrate the unstructured data coming
from SN with the structure data in PLM System?
September, 2014
October, 2014
November,2014
December, 2014
January, 2014
February, 2015
March, 2015
We will propose a data management policy to manage data flow coming
from unstructured source with the structure source.
From ontology engineering concepts we will try to use ontology merging,
ontology mapping and consistency analysis.
Outcomes:
One Publication on Integration of unstructured data with PLM System
Topic: Industrial Case Study
Topic Description:
Industrial case study will be perform in order to test our system, when,
where and for which product it is applicable?
This work will answer that how we can use the shared understanding
about product in different phases of product life cycle? How this
knowledge can be used to make useful decisions?
This will also address that when, where and for which product this
knowledge can be used?
In this regard the nature of the product plays very key role. For example
in product which follow evolutionary or prototype model, this shared
knowledge can be used in different phase of product life cycle.
For manufacturing product we can use while developing a new product
or want to make some advancement in the existing product.
It can be very useful in software industry, because a little effort is need
to release a new version of software after taking views from customers.
Outcomes:
One publication on using shared knowledge for the improvement of
product.
One publication on A multi agent system for product reputation
evaluation based on SN.
Thesis writing and Defense
Figure 1: High Level Blocked Diagram of Thesis
Web
Information Extraction
and Cleaning
Sentiment Analysis
Product Reputation
Evaluation
Integration of
Unstructured Data with
PLM
PLM
System
Sentiment Analysis for Product Reputation
1. Introduction:
People express their opinion about product on the World Wide Web (WWW) in different
ways. A customer may post product review on a product reviews websites, merchant website
and express their emotion on blogs, groups, discussion forums and twitter. The customer
views about a product on the WWW are present in two forms: in document from and sentence
form [4]. Most user generated contents on review sites and merchant sites are in document
form; however the user views and comments on blogs, discussion forums and twitter are in
sentence form. This information can be used by both customer and industrial organization to
make some decisions. For example if a customer wants to buy a product, it first checks the
product reviews of existing customer. Similarly organization can also use this information to
check product reputation, drawbacks, strength and market trend.
Sentiment analysis and opinion mining is used to find that whether opinion of customer about
a product is positive or negative. It is the broad area of natural language processing,
computational linguistics and text mining. Sentiment analysis is the study of opinion and
emotion expressed in text. S. Das et al [1] and R. M. Tong [2] have introduced Sentiment
Analysis for the first time in 2001 and the term opinion mining was first used by Dave et al [3]
in 2003. It is challengeable task to determine that the emotion expressed in a document or
sentence about a product are positive or negative. A lot of standalone applications are
developed to find sentiment i.e. for product reviews, news, tweeters and blogs [13], [14], [15],
[16], [17] some of which are explain in section 3. The sentiment analysis may be document
level, or sentence level [4]. In document level it usually considers that the document is
opinionated and generally over all polarity of document is classified as positive or negative. In
sentence level first it is identified that whether the sentence is subjective (opinionated) or
objective. If sentence is subjective then it is classified as a positive or negative.
For practical application it is not enough to identify that opinion about a product is positive or
negative. A more detailed analysis is needed in order to know the customer likes, dislikes,
product strengths, weaknesses and market trend. This detailed analysis can be used for the
improvement of product life cycle. For this purpose featured based sentiment analysis need to
be performed. A customer may express opinion about product, product features, attributes,
characteristics, parts or components. It is therefore needed to identify the target feature about
which customer express opinion and then determine that whether it is positive or negative.
Another more detailed study is needed to analyze comparative sentences. Sometime the
customer compares two products in a single sentence. It is needed to identify that which
features of which product the customer preferred. Several other detailed analyses can be done
in order to build knowledge.
We want to propose a system that extract data about product from social networks, clean it
and perform sentiment analysis in order to classify the opinion as positive or negative. A
multi criteria and multi-source model will be used to evaluate product reputation. This
reputation data will be integrated with PLM System in order to make decision during product
life cycle.
2. Related work:
Sentiment analysis is an emerging and challengeable research topic in recent years. Different
researchers worked on different aspect of this issue and several supervised, unsupervised,
machine learning and rule based methods are proposed. A lot of sentiment analysis
applications are developed for product reviews, blogs, forums and tweeter. The current work
on sentiment analysis can be classified as sentence, document and feature level sentiment
classification [4]. Bing Liu et al [14] proposed a number of techniques based on data mining
and natural language processing to identify the features of product on which opinion is
expressed and classify the opinion as positive or negative. Each features is then ranked
according to it frequency to provide a summary to customer. Some other researchers such as
Papescu and Etzioni [15] also used the same techniques in a different way for extraction of
features and opinion bearing words.
An attribute based sentiment analysis system, using semantic role labeling tool is proposed by
Hanxiao Shi et al [16]. This system provide visualization of product features to customer
which is built from all reviews about that product, this help the customer to make decisions
about the product. Another method proposed by Xiaojun Li et al [18] that also using
semantic role labeling tool to classify the opinion about camera in reviews.
Soo-Min Kim and Eduard Hovy [21] developed a system that takes a topic and text about the
topic from user and automatically finds the opinion holder and opinion expressed. In this
method a collection of positive and negative words is constructed and synonyms and anatomy
set is obtained from WordNet. This dictionary of word is then used to find the individual
polarity of the word, which is combined to find the overall polarity of the sentence.
Mikhail Bautin et al [19] proposed a method to mine opinion about some leaders and
countries from international news.
News and articles are extracted from online news
resources in nine different languages. Other languages are translated into English using IBM
WebSphere Translation Server (WTS). Then corpus is used to find opinion about some world
leaders (such as George Bush and Vladimir Putin) and countries ( i-e Egypt and Israel).
A Sentence level sentiment analysis system has been proposed by A. Khan et al [20] for
product reviews. In this method a bag of sentence is constructed from product reviews. Noise
from sentence is removed and spelling corrections are made. Each sentence is then classified
as a subjective or objective using both machine learning and lexical approaches. The
polarities of subjective sentences are then identified by using lexical dictionary such as
SentiWordNet in consideration with contextual feature of each word in sentence.
Most of these techniques are domain dependent and extract data from a specific source either
from product reviews sites, twitter or blogs. Most of the system uses its own lexicon
dictionary of opinionated word and perform sentiment at document level. Very few systems
perform word sense disambiguation and tackle the impact of conjunction, modifiers on
polarity at sentence level. All these systems are standalone application and is not integrated
with other system, it simply provide a summary to both customer and organization. We want
to propose a sentiment analysis system that will incorporate all social networks, and perform
sentiment at sentence level by incorporating conjunction, modifiers as well as word sense
disambiguation. This knowledge is then integrated with PLM System to construct a product
knowledge base, which will be used throughout product life cycle.
3. Proposed solution for sentiment analysis
We are going to propose a system that uses sentiment analysis to discover the opinion of
customer about products and services. We know that data on the Social Network is in
unstructured form therefore the data is first brought into a structure form so that a machine
can process it easily. For this purpose our system first extract data about product or service
from Social networks and store it in database. Our system perform sentiment at sentence level
therefore each sentence from database is given to part of speech tagger (POS), which identify
the different part of speech used in a sentence. Sentiment bearing part of speech such as
adjective verb, adverb and noun which carry the polarity are given to SentiWordNet [6] in
order to find the polarity score of each word. Before obtaining the polarity score the context
of word is identified in which it appear. The numeric polarity score of these parts of speech
are combined to know the overall polarity of the sentence. If negation words such as not,
don’t cannot are found then the system will reverses the polarity of sentence. On the other
hand if conjunction words such as and, or, but, however etc. are found then the system will
use conjunction handling heuristics and the polarity is calculated according to the conjunction
word used and feature discussed. The following figure show architecture of the propose
system, which consist of Text loader, POS Tagger, SentiWordNet Sentiment Classifiers
modules, Word Sense Disambiguation, WordNet and Conjunction Handling Heuristics, each
module is explain in the next section.
3.1 Text Loader:
This module extracts text about product and service from Social Network and stores it in
database. The views and comments about a product or service may be post on merchant site,
review sites, blogs, discussion forums and twitter. Therefore Different methods can be used
to extract information from these sources. The following methods can be used.
1) If the emotion is available on the merchant website then the organization has full
control on data and it is easy for them to accessed and manage the information.
2) Most reviews site allows both customers as well as organization to download product
reviews.
3) Emotion may be extracted from twitter about a particular topic using various tools
such Tweet Archivist and Tweet Seeker etc.
4) Twitter also provides an Application Programming Interface (API) that can be used to
collect tweets and other related data [9].
5) We can also write our own code using web crawling and web scraping in order to
extract data about a product from social networks.
6) If the merchant has already an account on tweeter then it can also downloads its all
tweets, replies, followers and direct messages.
3.2 Part of Speech(POS) tagger:
Part of speech tagger is used to tag each word in a sentence with it appropriate part of speech. A
subjective sentence is a sentence in which a customer express it opinion about a product or service.
This sentence can include verb, noun, adjective, adverb, negation, conjunction which are very
important in sentiment analysis. Adjective, verb and adverb are actual sentiment bearing words and
on the bases of these parts of speech we can find the overall polarity express in whole sentence.
However other words such as negation, conjunction, enhancer and reducer can also modify the
polarity, so these part of speech must also be handle. Part of speech tagger is used to identify different
part of speech. Sentiment bearing words are identified so that we can find the individual polarity score
from SentiWordNet. Negation, conjunction and modifier are discovered to change the polarity
according to the modifier word used. The following table show some tags assign by part of speech
tagger to different words.
Table 1: Tags of POS Tagger
No
1
2
3
4
5
Part of Speech
Adjective
Verb
Adverb
Noun
Conjunction
POS Tagger Abbreviation
JJ
VB
RB
NN
CC
SentiWordNet Abbreviation
A
V
R
N
_
3.3 SentiWordNet:
SentiWordNet [6] is a database of opinionated words derived from WordNet [7]. It is publically
available for researcher. In this database each word is associated with its polarity score representing
positive and negative sentiment. Most sentiment analysis system uses SentiWordNet to classify
sentiment. Our system also uses SentiWordNet to find polarity of the opinionated words. Our system
gives adjectives, verbs, adverbs and nouns to SentiWordNet in order to find their associative polarity
scores.
3.4 Word Sense Disambiguation:
We know that a word may be used in many senses. Therefore in SentiWordNet a word may have
many polarity score depending on the context of the word in which it can be used. A word may have
positive polarity score if used in one context conversely it may have negative polarity score if used in
another context. Therefore Before obtaining polarity score from SentiWordNet we need to identify the
context in which the word appears. This identification of the context of the word in a sentence is refers
to sense disambiguation. Most of the research on sentiment analysis focused on finding opinion out of
context [22]. However now the researchers try to classify the opinion by considering the context of
the word [20, 22, 23, 25]. We will use the method proposed by A. Khan with some changes [20]. In
this method the POS tag pattern of the sentence is match with all possible senses extracted from
WordNet glossaries. The system will first look for exact match and extract the polarity score of that
sense from SenitWorNet. In the absence of exact match the system will go for nearest match and will
extract the polarity score of that sense from SentiWordNet. In this way we are able to obtained polarity
score of word from SentiWordNet according to the context in which it appears.
3.5 Sentiment Classifier
This module is divided into verb sentiment classifier, noun sentiment classifier, adjective sentiment
classifier and sentence sentiment classifier sub modules. Verb, noun and adjective sentiment classifier
classifying by the polarity score of corresponding part of speech using SentiWordNet. The polarity
score is a numeric value that can be positive or negative and it is obtain from database of opinionated
words called SentiWordNet. However before obtaining the polarity score we need to identify the
context in which the word is used, so we can obtain the right polarity score corresponding to that
context. Sentence sentiment classifier combines the polarity of adjectives verbs, adverb and nouns in
order to find the overall polarity of the sentence. In the presence of negation and conjunction the
sentence polarity is readjusted according to negation and conjunction heuristics.
3.6 Negation
Negations are those words that reverse the polarity of the sentence if found. These words such
as No, not, don’t, never, wouldn’t, can’t, doesn’t etc are very important in finding the polarity.
If these words are found then our system reverse the polarity of the sentence. For example if
the polarity is 0.45 and a negation word is found then the system will reverse it to -0.45.
3.7 Conjunction:
Conjunctions are those words that combine words and phrases in a sentence. Example of
conjunction words are AND, OR, BUT, HOWEVER, SIMILARLY etc. The conjunctions
words has a substantial impact on the overall polarity of the sentence, this issue is address by
Arun Meena and T.V. Prabhakar [8] and A.Khan [20] in his papers. It is very challengeable
task to identify the polarity of the whole sentence in presence of these words. Current research
work only focus on classifying the opinion in the presence of conjunction without considering
the feature. Researchers try to find the whole sentiment express in a sentence. If a single
feature of an object is discussed then this method works fine. However when opinion about
more than one features is expressed then it is not useful to classify the whole sentence as
positive or negative. Therefore if opinion is expressed about multi feature in the presence of
conjunction then our system consider that multiple opinions are expressed in a single sentence
about multi features and therefore we need to classify each separately. For example in
sentence “The screen of this mobile is good but the battery life is short” the conjunction word
“but” is used here. In this sentence opinion about scree and battery is expressed, therefore we
need to find the opinion about each feature separately.
In the presence of conjunction word our systems first find that whether one or more than one
feature is discussed in a sentence. If one feature is discussed then our system used the rules
proposed by Meena et al [8] for conjunction to find the overall polarity of the sentence. On
the other if multiple features are discuss then our system find the polarity of each phrase
separately.
3.8 Feature based sentiment analysis:
In feature based sentiment analysis a more detailed analysis is performed. In this model first it
is identified that about which feature of product the customer express their opinion and then it
is classified as a positive or negative. From this analysis we can identify the customer likes
dislikes and strength and weakness of the product. The customer usually uses noun and noun
phrases to talk about product features, however sometime adjective can also be used.
Therefore we can extract product feature by incorporating these part of speech. There are two
types of feature; one is called frequent and another one is infrequent feature [26]. Frequent
feature are those feature about which many customer express their opinion. On the other hand
the feature about which few customer talk about is called infrequent. Frequent feature are
easier to identify than infrequent because it appear many times. Association mining method
[27] can be used to identify all frequent features as used by [26]. Different customer may use
different word to talk about the same feature therefore we can also obtain the synonym set of
the frequent feature from WordNet to build a dictionary of frequent features.
4. Sentiment analysis of comparative sentences:
Evaluation of a product can be done in two ways, direct appraisal and comparison [4]. In
direct opinion in a single sentence about a single product is expressed. In comparative
sentence two or more competitive products are compared. It is very useful to extract such
sentences in order to identify which product the customer prefer and which feature of which
product the customer likes or dislikes.
5. Competitor Analysis:
Most organization arranges survey or customer poll to evaluate their own as well as
competitor products. Therefore competitor analysis can also be very useful in order to know
about competitor. The same sentiment analysis and feature based sentiment analysis can also
be performed for competitor in order to know the strength and weakness of competing
product. This information can also be very useful for the improvement of different phases of
product life cycle.
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