The growth of social networking has gained much interest from the research community in recent years. Social networking technology as an e-learning tool seems promising for education instructors to combine distance education. Several analysis researches of social media were conducted for detection opinion leaders. While most of the existing algorithms proposed for communities determination are destined to commercial use, in this work, we present a new approach for detecting opinion leaders based on analyzing online learning community interactions. In fact, we aim to identify learners behaviors and attitudes in social network sites as productive online tools for learning. To achieve this purpose, we describe a method of performing detecting opinion leaders by using machine learning techniques. We focus on the application of text mining and sentiment analysis. The output of this work prove that education-based social network is very eective and improvement for online communications. Experiments show the eciency of the introduced method which can be helpful and protable for education instructors.
Researchers have become interested to analyse social network site(SNS) and
communities detection to explore the educational benets of using it. Using social
network, we construct a virtual communities enable learners to connect and collaborate
on international platforms, exceeding geographical boundaries [
In the shared forum provided by an SNS, learners can construct a shared
understanding and engage and collaborate in discussions, while sharing common resources,
such as readings, links, and videos [
The integration and use of social networking technology as an e-learning tool
seems promising for distance education. In [
The rest of this paper is organized as follows. Section 2 discusses the related works; Section 3 presents the research framework; Section 4 presents experimental results; in Section 5, concludes the paper with some suggestion for future research directions. 2
Social networks analysis has recently gained more importance thanks to its
application in elds such as sociology. Many researchers are interested on identication
of opinion leaders. So far, some researchers were studied on opinion leaders where
rst of them is situated within the eld of sociology. Numerous studies such as
[
The studies on opinion leader identication can be divided into two parts. First,
the link-based opinion leader identication methods that consider the social
interactive structure of the network. Second, the mixture of opinion leader identication
methods that combine the social link information with semantic-based information
embodied in documents [
As an example of link-based opinion leader selection, the authors in[
Also, in[
In case of social network, many of the trust of customers to the social network
communities are based on opinion leaders’ recommendations for products and
services [
This section presents our methodology. Firstly, the general architecture is presented. Secondly, the extraction and the collection of the raw data are explained. Then, the process of sentiment analysis of online learning communities and the detection of opinion leader are discussed. With the widespread adoption of social media, online learning communities are perceived as a network of knowledge comprised of interconnected individuals with varying roles. Opinion leaders are important in social networks because of their ability to inuence the attitudes and behaviours of others. The introduced work is based on the analysis of learning communities in social network namely Facebook. In this study, we chose the type group ’We Read for you’( On a lu pour vous (OLPV))3. We used the programming interface available on Facebook site, called Facebook API, providing tools and applications that allowed us to access and get the necessary for information our study. Unlike closed and secret groups, the extraction of database from learning community ’OLPV’ group is permitted. This later group has 13,537 members and allows its members to discuss and exchange views on issues related to learning especially reading. Posts the online learning community OLPV are grouped by interest and has many forms namely "status", "photo", "video", "URL" and "event". These posts have multiple messages types:
An interrogative message where the user requests the opinions of others, Quote, poem or proverb message, a message where the user expresses his opinion or a message without an opinion.
3.2
Machine Learning and Opinions extraction We introduce machine learning method namely Fuzzy SVM for mining opinions of social network learners. Fuzzy SVM is explored for multi-class scenarios (positive, negative, fuzzy). In fact, we propose a renement which aims to consider the spring’s variables which refer to the membership functions of fuzzy classes. This later is dened as follows: i = w;b (Yifw;b(xi))+ (1) where (x)+ represents the positive part of x 2 R. This quantity is related to the concept of margin.It measures the location of a pair ( Xi, Yi) in relation to the margin of the classier fwb. Therefore, three cases are introduced: 3 https ://www.facebook.com/groups/oalpv.tn/members/ min 1=2kW k2 + C yi(wT (Xi + b)) 1
i i n X si i i=1 if i > w;b so the pair (Xi, Yi) is misclassied by fw;b if 0< i < w;b so so the couple is well ranked but close to the hyperplane (the distance from the hyperplane is less than the margin) i = 0 so the couple is in its majority class (the distance to the hyperplane is greater than the margin).
Indeed, the introduction of these springs variables allow to relax the mathematical formulation of our fuzzy SVM. While the main classes treated on sentiment analysis in social networks are the positive and negative opinion, in our research we focus on fuzzy aspects. To solve this problem, we integrate fuzzy class to labeled input terms. In this context, the training samples are :
S = f(Xi; yi; si); i = 1; :::N 2 RN g Where each Xi 2 RN denotes a training sample and yi 2 {-1,1} represents its class label. si = {1,2,...N} present our fuzzy class with si 1 and a constant 0.Denote a set as:
Q = fXij(Xi; yi; si) 2 Sg Clearly, it contains two classes. One class contains positive opinion such sample point Xi with yi=1, denoting this class by C+, then,
C+ = f(Xij(Xi 2 S)gandyi = 1 The negative class contains such sample point Xi with yi=-1, denoting this class by C , then,
C = f(Xij(Xi 2 S)gandyi =
1
Q = C+ [ C Thus, the mathematical formulation for our sentiment classication problem and the linear Fuzzy Support Vector Machine classier can be obtained by solving the following optimization problem: (2) (3) (4) (5) (6) (7) (8) where C denotes a constant, the fuzzy class si is the fuzzy labeled opinion terms Xi toward one class and the parameter i the percentage of opinion class , the term si i can be looked as a measure of degree of fuzziness.
Once opinions are detected, a score opinion ScoreOp is assigned . This score is weighted or equal to the number of words contained in the document. 3.3
Detection of inuential learners The detection of online inuential learners, we opted for an assessment score corresponding to the frequency of opinion terms contained in each learner message. In addition, to express score of inuential learners, we opted a threshold below which the inuence of the post is not signicant. This threshold is equal to the average of the ScoreOp obtained for all posts.
In other words, any post having a "ScoreOp" higher than or equal to the dened threshold, will be considered as inuential leaders. Figure 3 depicts extracted posts from online learning community and presented opinions leaders learners.
Here we present the experimental results of the introduced opinion leaders method on both learning communities OLP V 1 and OLP V 2. We use Graph API 4 to extract real time data from these two later communities. In fact, the use of data from the same learner group (OLPV) in two dierent periods allows us to show the position of opinion leaders over time.
The inuential learners belonging to community OLPV1 are shown, in ascending order, in Figure 4 4 https ://developers.facebook.com/docs/graph-api
Using graph API the ID of the inuential learners, we can extract information about the opinion leader’s as name and published message. The following gure shows the opinion leader having the highest ScorInf l from the OLPV1 learning community. Figure 6 shows the diagram of inuential learner sorted by second learning community OLPV2.
ScoreInf l of the Moreover, we use precision and recall to validate the utility of the introduced method. The precision measures the exactness of a classier and recall represents the number of correct classications penalized by the number of missed items. These two measures are calculated as follows:
P recision = I \ N I
N I Recall = N I \ I
I (10) (11) where I denotes the number of outputs inuential learners, N I represents the number of outputs non-inuential learners. Figure 8 depicts information related to inuential learner in OLPV2 community. According to the curve above, which present the recall and the precision our method gets a higher precision but a lower recall. We can conclude that the performance of the proposed methodology is good. 5
In this work, we have investigated the utility of opinion mining on a novel collection of dataset which is online learning communities. Using the most well-known machine learning algorithm namely SVM, we identify inuential learners. First of all, we analyse popularity and reputation of posts to determine which are more important than others referring to the frequency of opinion words in learning posts. Then, we identify inuential posts leading to detect opinion leaders learners.
The overall performance of the proposed methodology is satisfactory, however, we would like to further improve our research needs to explore how SNSs can be used most eectively in distance education courses as a technological tool for improved online communications among students in higher distance education courses.