Puneet Joshi is currently pursuing bachlelor degree program in Information technology engineering in Malwa Institute of Technology | RGTU, India, PH-09630974205. E-mail: puneetjoshi@hotmail.comInternational Journal of Scientific & Engineering Research, Volume 3, Issue 6, June-2012 1
ISSN 2229-5518
Verification and Validation of Comments in
Personal Learning Network(s)
Puneet Joshi, Sheetal Sharma, Meer Shizan Ali, Divya Gautam
Abstract— Internet plays a vital role in information sharing. One can type in any word and fetch the data instantly. PLN targets the intellectual geeks and facilitates them by providing all the associated information together on one platform. PLN is a platf orm where one can upload, share and download videos. One can also upload text documents, images, etc. The user maintains his own profile by logging- in. They can start their own discussions by asking questions and can comment on the resources can be shared and downloaded, a lso their comments and reviews will be verified and validated. All resources are available at one common place thus saving users time. It is always a matter of concern for developers to decide on what technology will prove the most efficient for implementing the project. To justify the concept, this project is made in two different technologies namely J2EE with third parties as Struts, Hibernate and Java Script and the other as the basic J2EE technology.
Index Terms— PLN, Java, Struts, Hibernate, JSP, Data structures, MySql,, comments, bad comments.
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nternet plays a vital role in information sharing. One can type in any word and fetch the data instantly. PLN targets the intellectual geeks and facilitates them by providing all the associated information together on one platform. PLN is a platform where one can upload, share and download videos. One can also upload text documents, images, etc. The user maintains his own profile by logging-in. They can start their own discussions by asking questions and can comment on the resources can be shared and downloaded, also their comments and reviews will be verified and validated. All resources are available at one common place thus saving users time. It is always a matter of concern for developers to decide on what technology will prove the most efficient for implementing the project. To justify the concept, this project is made in two dif- ferent technologies namely J2EE with third parties as Struts, Hibernate and Java Script and the other as the basic J2EE tech-
nology.
PLN aims to promote a greater count of elites to
splurge into the field of Technology. It integrates the benefits
of learning with the convenience of a ‘no-physical-bar ’ virtual learning environment, minus the commuting hazards and ex- penses. It will usher in the immense flexibility and sophistica- tion in the existing learning platform structures, with the per- fect blend of synchronous and asynchronous interaction. It provides a means of collaborative learning for it users.
PLN is an online service or platform that focuses on building
and reflecting of educational networks among people who
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Puneet Joshi is currently pursuing bachlelor degree program in Information technology engineering in Malwa Institute of Technology | RGTU, India, PH-09630974205. E-mail: puneetjoshi@hotmail.com
Sheetal Sharma is currently pursuing bachlelor degree program in Informa-
tion technology engineering in Malwa Institute of Technology | RGTU, India, PH-07566268306. E-mail: sheetal96@live.com
Meer Shizan Ali is currently pursuing PhD from CMJ University,Shillong
Divya Gautam is Head of Department I.T. in Malwa Institute of Technolo- gy, Indore. E-mail: divyagautam06@gmail.com
share same interests and activities.
PLN aggregates videos in different categories and allow users to collaborate on them by sharing, commenting and recom- mending to other users. Video Sharing is a type of service that allows anyone to upload videos and share them with the world.
There are discussion forums where a user can start a
discussion by posting queries. Users are free to answer and discuss. They can also suggest edits to existing answers pro- vided by other users. One of the time consuming things for many to take care of on a PLN is moderating the bad language and comments from people attacking your POST. The moderation blacklist allows you to select words that will always be banned from your forum. Add as many as you like and they will never appear again. You don’t even have to choose rude words because they will automatically be removed depending on how you set your profanity blacklist.
A quiz is posted daily as ‚question of the day‛ which enables the participants to explore new technologies.
There is a section proving details on various competi-
tive exams which a student can take after engineering.
The placement cell provides updates on recruitment
processes of various companies going on in the premises. Also availale are the questions asked in the interviews.
Users can maintain their personal accounts by register-
ing and logging-in.
The implementation of the same concept in two different tech-
nologies will flair out the solution for the question of the better technology to work in this situation taking into account all aspects.
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We have extensively studied the characteristics of comments in discussion forums and the technology used. In order to counter technology, bad commentors do change their mode of operation.These evaluations help us to enhance the existing technology and thereby help us to combat bad comments ef- fectively. We observed that bad commentors use different combinations to post the comments.
Implementation of the code is done using the MVC architec- ture by struts and hibernates to optimize the entire work.
The xml mapping of struts-config. Is shown, the bad comment validator is in the discussion forum part of the application, where the expert advice and doubt discussions are being done in the form of queries and replies.
To prevent bad words from sites which allows commenting, blog filter can be used. This supports a reliable protection to the blog, forum & guestbooks owners. It works with detecting bad words in a comment and finding the probability whether the comment is bad or not. Incoming comment and earlier comments stored in database are used to derive this formula. To bypass comment fil- ters, notorious users use punctuations, capital letters in between
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small letters and special characters to modify the meaning to the filters. To avoid this, the words that are sent from users are counted and unneccesary words like punctuation and special cha- racters elimininated. After this elimination process, the entire user comment should be stored in database in lower case format.
7. ALGORITHM
The following algorithm shows the structure of our unwanted comment blocking methodology.
We selected a code to derive a formula to detect whether the given comment is bad comment or not. We derived this for- mula carefully to avoid false positive and false negative com- ments. A legitimate comment may contain a couple of bad words.
Also, a comment may contain some good words and this does not mean that the comment is legitimate. Therefore, a probability mechanism is designed to distinguish incoming comments. We derived this code to block incoming comments from the be used along with the existing data in the database end-users. To implement this code, the administrator should store sample bad comment words in the database. The incom- ing comment will.
The first 5 steps mentioned in this algorithm are basic programming techniques, so we are not going to explain them here. We are going to deal directly with the mechanism to identify the comment. Our first step is to establish a connec-
tion between the database and our comment page.
İn Step 1, the system asks users to enter their name & email
address. The entered names and email addresses are verified
for its validity before connecting to the database and web
page. Without proper name & mail address, users are not al-
lowed to enter a comment. To support dynamic user participa-
tion, the users are encouraged to post their comment without
registering into the local host. After submitting, the page is
directly connected to the database.
In Step 2, the users IP address shoud be noted and stored in the database. İn the next step, unwanted characters like punc- tuations and special characters should be removed.
Users mix lower case, uppercase characters and punctuations to bypass the filters. To prevent this, all incoming user’s com- ments will be free from punctuations and all words will be converted to lower case.
In Step 3, comments should be eliminated from unnecessary words. Those words are not counted as bad. However, they are counted from the bad word filter. To increase the perfor- mance of the bad comment filter, unnecessary words should be ignored. These words must be defined by the programmer. Selection of those words depends on the programmer.
In Step 4 of the algorithm, unnecessary words should be re- moved & total number of words counted. The unnecessary words include pronouns- like‘am','is','are','he','she','it','you','we','they','i','have','has','had',' and','us','do','does','did','was','were','a','an','in','on','the','to','but'
,'of','from','them','also','their' will be removed. The administra- tor can define any such words by himself. This part of the program shows the indication of unnecessary words and eli- mination of those words from the comment text.
'had','and','us','do','does','did','was','were','a','an','in','on','the'
,'to','but','of','
from','them','also','their');
dizi=explode(" ",comment_db);
foreach(dizi as words){
counter=0;
foreach(extras as gextras){
if(gextra==words)
counter++;
}
if(counter==0)
gwords[words]++;
}
foreach(gwords as key=>value){
print(key." = ".value." ");
sor="insert into table2( form_id, words, adet) values ( '1',
'key','value' )";
register= mysql_query( sor );}
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bad_words=array( 'idiot' , 'stupid' , 'bad' , 'awful' , 'terrible' ,
'disgusting' , 'silly' , 'freak' , 'fool' ,'rubbish' ,'ugly' );
// more can be included in the array.
count=str_word_count(comment_db);
dizi=explode(" ",comment_db);
foreach(dizi: words){
foreach(bad_words : bad){
if(bad==words)
bad_count++;}
}
print(" bad words in our comments:");
print(bad_count);
In the scenario of PLN(s) and discussion forums, we have seen bad comments on some useful and important information. We implemented a module which is capable enough to verify and validate the bad abusive words from the comments of experts in our expert panel doubt discussion forum. This verification of comments will be real time. Which will stop the bad words in the important discussions and will enhance learning.
No References.
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