Chalothorn, Tawunrat

Ellman, Jeremy

English

Northumbria Research Link

Northumbria Research Link
Citation:  Chalothorn,  Tawunrat  and  Ellman,  Jeremy  (2012)  Using  SentiWordNet  and  Sentiment 
Analysis for Detecting Radical Content on Web Forums. In: 6th Conference on Software, Knowledge, 
Information Management and Applications (SKIMA 2012), 9-11 September 2012, Chengdu University. 
URL: 
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Using SentiWordNet and Sentiment Analysis for Detecting Radical 
Content on Web Forums 
 
Tawunrat Chalothorn and Jeremy Ellman 
 
Computing, Engineering & Information Sciences, University of Northumbria at Newcastle,  
Newcastle Upon Tyne, United Kingdom 
 
Abstract—The internet has become a major tool for 
communication, training, fundraising, media operations, and 
recruitment, and these processes often use web forums. This 
paper presents a model that was built using SentiWordNet, 
WordNet and NLTK to analyze selected web forums that 
included radical content. The approaches of the model measure 
and identify sentiment polarity and affect the intensity of that 
which appears in the web forum.  
 
Index Terms—SentiWordNet, sentiment, analysis, web 
forums, radical 
I. INTRODUCTION 
Web forums have become important places for social 
communication and discussion on the internet. Some radical 
groups also use them for communication and disseminating 
their ideologies to the public [1]. The terrorists' main goals in 
using the internet are often research, communication, training, 
fundraising, media operations, radicalization and recruitment 
[2]. This research presents the system approach of two web 
forums in the area of sentiment and affects analysis. 
Many people have questioned why this research was carried 
out. The reason is that the United Kingdom’s parliament has 
enacted an anti-terrorism law, the Terrorism Act 2006 [3 and 
4], which extends the government’s ability to outlaw terrorist 
organizations that promote and encourage or may be thought 
to encourage terrorism [5]. In 2007 they launched the ‘Prevent 
Strategy’ to prevent the radicalization of youths in Great 
Britain and block networks that support terrorists [6]. The 
internet has become the main tool used by terrorists since it 
can be accessed anywhere and it gives access to a wide 
spectrum of ideological material that may be translated into 
multiple languages [7]. 
This paper is structured as follows: Section II provides 
some discussion on work related to sentiment analysis and 
SentiWordNet. SentiWordNet is a lexical resource that 
supports opinion mining by assigning a positivity score and a 
negativity score to each WordNet. Section III discusses the 
research question. Data collection and the system technique 
were described in section IV. Finally, results analysis are 
presented in sections V. 
II. RELATED WORK 
The term ‘sentiment’ was used by [8] and [9] in reference 
to the automatic analysis of evaluative text, and the tracking of 
predictive judgments and analysis of market sentiment in [10]. 
After that, the term ‘opinion mining’ was brought to the 
WWW conference by [11]. They mentioned that the ideal 
opinion-mining tools would press a set of search results for a 
given item, generating a list of product attributes and 
aggregating opinions about each of them [10]. Sentiment 
analysis has been considered in many research fields, such as 
[12] where sentiment analysis was used to analyze video 
comments and user profiles. In [13], the structure of lexical 
contextual sentences was used to classify sentiment 
classification from online customer reviews. In [14], 
SentiWordNet was used for classifying movie reviews in 
German. In addition, SentiWordNet was used in [15] for 
sentiment classification of reviews. As far as we are 
concerned, there are some papers that have used data from 
websites, blogs and forums but they have conducted testing 
using Machine Learning and there are no existing papers that 
have used data from radical web forums for testing with 
SentiWordNet. 
III. RESEARCH QUESTIONS 
Opinions and emotions are used on the internet for 
communication and can be related to and involve radical 
ideologies. This paper presents our research on sentiment 
analysis and the detection of radical content. In particular, this 
research analyzes an existing technique in an attempt to 
answer the research question ‘How effective is SentiWordNet 
for detecting opinions and emotions on the internet?’ 
IV. METHODS 
Two forums were selected for use in the research: Montada 
and Qawem. Both of them use the Arabic language. 500 
sentences of each forum were translated manually for use in 
the experiment. Model building was written using Python 
programming language. The model building phase was started 
by splitting sentences into words and reducing the high-
frequency text (stopwords) in the sentences. Words were 
stored in a bag of words (BOW) and part of speech (POS) was 
used for tagging words and knowing the position of each word 
in the sentence. Lexicon, WordNet and SentiWordNet were 
used for assigning positive and negative scores of each synset 
in each word [7].  
The formulas for calculating positive and negative scores 
were taken from [16], as shown in (1) and (2). The final scores 
of sentences were calculated using a formula taken from [8], 
as shown in (3). The scores of sentences were applied using 
the rule that if the sentence had a positive score more than or 
equal to its negative score, then the sentence would be 
classified as positive. Otherwise it would be negative.  
            [
   
      
] 
            [
   
      
] 
    is the number of lemma that have    ( )( )  
   ( )( ) and     ( )( )   ;     is the number of lemma 
that have    ( )( )     ( )( ) and     ( )( )   ; and 
       is the total number of lemma in synsets.  
                [
∑      ( )    
 
] 
               is the positive or negative scores of 
sentences;      ( ) is the positive or negative scores of the 
word in sentences;       is the number of words in sentences. 
 
Fig. 1.  Overall process of the system 
V. RESULTS 
The model building of sentiment was applied to the web 
forums Montada and Qawem for analysis of the results. After 
removing stopwords, the rest of the sentences were used for 
analysis. The search function in the system was used to extract 
statistics of corpus for getting information about the frequency 
of words that were used in the forums. The content in the 
forums was expected to be manipulated by religion and 
ideology. In the comparison between Qawem and Montada, it 
was found that Qawem contained more words related to 
radical ideology than Montana. In the results of the sentiment 
analysis of postings as percentages show that the Montada 
forum has less negative postings than the Qawem forum. In 
particular, the radical affect is quite strong in the 
communication found in the Qawem forum. 
VI. CONCLUSION 
In this research we have presented an analysis of two web 
forums, Montada and Qawem. The approach of model 
building and the results were explained. Overall, the results 
show that Qawem has more radical content than Montada. For 
future work, a comparative human evaluation can take place. 
We will ask people to rate sentences and see how their 
opinions on a rating scale compare to those of the model.  
REFERENCES 
[1] J. Glaser, J. Dixit, and D. P. Green, "Studying Hate Crime with the 
Internet: What Makes Racists Advocate Racial Violence?" Journal of 
Social Issues, vol. 58, pp. 177-193, 2002. 
[2] R. Tong, "An operational system for detecting and tracking opinions on-
line," presented at the In Proc of SIGR Workshop on Operational Text 
Classification, New Orleans, Louisiana, 2001. 
[3] HM Government. (2006). Terrorism Act 2006. Available: 
www.legislation.gov.uk/ukpga/2006/11/section/1 
[4]  J. C. Paye and J. H. Membrez, Global war on liberty: Telos Press Pub., 
2007. 
[5] E. Parker, "Implementation of the UK Terrorism Act 2006 - The 
Relationship between Counterterrorism Law, Free Speech, and the 
Muslim Community in the United Kingdom versus the United States," 
Emory International Law Review 21 Emory Int'l L. Rev., pp. 711-758, 
2007. 
[6] G. Morgan, "Government to block terrorist web sites," in 
computing.co.uk, ed, 2011. 
[7] P. Nessera, "Ideologies of Jihad in Europe," Terrorism and Political 
Violence, vol. 23, pp. 173-200, 2011. 
[8] S. Das and M. Chen, "Yahoo! for Amazon: Extracting market sentiment 
from stock message boards," presented at the Proceedings of the Asia 
Pacific Finance Association Annual Conference APFA, 2001. 
[9] R. Tong, "An operational system for detecting and tracking opinions on-
line," presented at the In Proc of SIGR Workshop on Operational Text 
Classification, New Orleans, Louisiana, 2001. 
[10] B. Pang and L. Lee, "Opinion Mining and Sentiment Analysis," Found. 
Trends Inf. Retr., vol. 2, pp. 1-135, 2008. 
[11] K. Dave, S. Lawrence, and D. M. Pennock, "Mining the peanut gallery: 
opinion extraction and semantic classification of product reviews," 
presented at the Proceedings of the 12th International Conference on the 
World Wide Web, Budapest, Hungary, 2003. 
[12] A. Bermingham, M. Conway, L. McInerney, N. O'Hare, and A. F. 
Smeaton, "Combining Social Network Analysis and Sentiment Analysis 
to Explore the Potential for Online Radicalisation," presented at the 
Proceedings of the 2009 International Conference on Advances in Social 
Network Analysis and Mining, 2009. 
[13] A. Khan and B. Baharudin, "Sentiment classification using sentence-
level semantic orientation of opinion terms from blogs," presented at the 
National Postgraduate Conference (NPC), 2011. 
[14] K. Denecke, "Using SentiWordNet for multilingual sentiment analysis," 
presented at the Data Engineering Workshop, 2008. ICDEW 2008. IEEE 
24th International Conference, 2008. 
[15] A. Hamouda and M. Rohaim, "Reviews Classification Using 
SentiWordNet Lexicon," The Online Journal on Computer Science and 
Information Technology (OJCSIT) vol. 2, pp. 120-123, 2011. 
[16] A. Neviarouskaya, H. Prendinger, and M. Ishizuka, "Textual Affect 
Sensing for Sociable and Expressive Online Communication," presented 
at the Proceedings of the 2nd International Conference on Affective 
Computing and Intelligent Interaction, Lisbon, Portugal, 2007. 
 
 
 
 


Using SentiWordNet and Sentiment Analysis for Detecting Radical Content on Web Forums

Citation:  Chalothorn,  T.  and  Ellman,  J.  (2012)  'Using  SentiWordNet  and  Sentiment  Analysis  for  Detecting  Radical  Content  on Web Forums'.  In:  in  proceedings of the  6th  Conference  on  Software,  Knowledge,  Information  Management  and  Applications (SKIMA  2012). 9 - 11 September 2012, Chengdu University.Published by: Chengdu ChinaURL: This  version  was  downloaded  from  Northumbria  Research  Link:  http://nrl.northumbria.ac.uk/13075/Northumbria  University  has  developed Northumbria  Research  Link  (NRL)  to  enable users to access the University’s research output.  Copyright  © and moral  rights  for  items on NRL  are retained by the individual  author(s) and/or other  copyright  owners.  Single  copies of full  items can be reproduced, displayed or performed, and given to third  parties  in  any  format  or  medium  for  personal  research or  study,  educational,  or  not-for-profit  purposes without  prior  permission  or  charge,  provided  the  authors,  tit le  and  full  bibliographic  details  are  given,  as  well  as  a  hyperlink  and/or  URL  to  the  original  metadata  page. The content  must  not  be changed in  any way.  Full  items must  not  be sold commercially  in  any format  or medium  without  formal  permission of the copyright  holder.  The full  policy is available online: http://nrl.northumbria.ac.uk/policies.htmlThis document  may differ  from the final,  published version of the research and has been made available online in  accordance with  publisher  policies. To read and/or cite from the  published  version  of the  research,  please visit  the  publisher’s  website  (a subscription  may be required.)Using SentiWordNet and Sentiment Analysis for Detecting Radical Content on Web Forums  Tawunrat Chalothorn and Jeremy Ellman  Computing, Engineering & Information Sciences, University of Northumbria at Newcastle,  Newcastle Upon Tyne, United Kingdom  Abstract—The internet has become a major tool for communication, training, fundraising, media operations, and recruitment, and these processes often use web forums. This paper presents a model that was built using SentiWordNet, WordNet and NLTK to analyze selected web forums that included radical content. The approaches of the model measure and identify sentiment polarity and affect the intensity of that which appears in the web forum.   Index Terms—SentiWordNet, sentiment, analysis, web forums, radical I. INTRODUCTION Web forums have become important places for social communication and discussion on the internet. Some radical groups also use them for communication and disseminating their ideologies to the public [1]. The terrorists' main goals in using the internet are often research, communication, training, fundraising, media operations, radicalization and recruitment [2]. This research presents the system approach of two web forums in the area of sentiment and affects analysis. Many people have questioned why this research was carried out. The reason is that the United Kingdom’s parliament has enacted an anti-terrorism law, the Terrorism Act 2006 [3 and 4], which extends the government’s ability to outlaw terrorist organizations that promote and encourage or may be thought to encourage terrorism [5]. In 2007 they launched the ‘Prevent Strategy’ to prevent the radicalization of youths in Great Britain and block networks that support terrorists [6]. The internet has become the main tool used by terrorists since it can be accessed anywhere and it gives access to a wide spectrum of ideological material that may be translated into multiple languages [7]. This paper is structured as follows: Section II provides some discussion on work related to sentiment analysis and SentiWordNet. SentiWordNet is a lexical resource that supports opinion mining by assigning a positivity score and a negativity score to each WordNet. Section III discusses the research question. Data collection and the system technique were described in section IV. Finally, results analysis are presented in sections V. II. RELATED WORK The term ‘sentiment’ was used by [8] and [9] in reference to the automatic analysis of evaluative text, and the tracking of predictive judgments and analysis of market sentiment in [10]. After that, the term ‘opinion mining’ was brought to the WWW conference by [11]. They mentioned that the ideal opinion-mining tools would press a set of search results for a given item, generating a list of product attributes and aggregating opinions about each of them [10]. Sentiment analysis has been considered in many research fields, such as [12] where sentiment analysis was used to analyze video comments and user profiles. In [13], the structure of lexical contextual sentences was used to classify sentiment classification from online customer reviews. In [14], SentiWordNet was used for classifying movie reviews in German. In addition, SentiWordNet was used in [15] for sentiment classification of reviews. As far as we are concerned, there are some papers that have used data from websites, blogs and forums but they have conducted testing using Machine Learning and there are no existing papers that have used data from radical web forums for testing with SentiWordNet. III. RESEARCH QUESTIONS Opinions and emotions are used on the internet for communication and can be related to and involve radical ideologies. This paper presents our research on sentiment analysis and the detection of radical content. In particular, this research analyzes an existing technique in an attempt to answer the research question ‘How effective is SentiWordNet for detecting opinions and emotions on the internet?’ IV. METHODS Two forums were selected for use in the research: Montada and Qawem. Both of them use the Arabic language. 500 sentences of each forum were translated manually for use in the experiment. Model building was written using Python programming language. The model building phase was started by splitting sentences into words and reducing the high-frequency text (stopwords) in the sentences. Words were stored in a bag of words (BOW) and part of speech (POS) was used for tagging words and knowing the position of each word in the sentence. Lexicon, WordNet and SentiWordNet were used for assigning positive and negative scores of each synset in each word [7].  The formulas for calculating positive and negative scores were taken from [16], as shown in (1) and (2). The final scores of sentences were calculated using a formula taken from [8], as shown in (3). The scores of sentences were applied using the rule that if the sentence had a positive score more than or equal to its negative score, then the sentence would be classified as positive. Otherwise it would be negative.              [         ]             [         ]     is the number of lemma that have    ( )( )     ( )( ) and     ( )( )   ;     is the number of lemma that have    ( )( )     ( )( ) and     ( )( )   ; and        is the total number of lemma in synsets.                  [∑      ( )     ]                is the positive or negative scores of sentences;      ( ) is the positive or negative scores of the word in sentences;       is the number of words in sentences.  Fig. 1.  Overall process of the system V. RESULTS The model building of sentiment was applied to the web forums Montada and Qawem for analysis of the results. After removing stopwords, the rest of the sentences were used for analysis. The search function in the system was used to extract statistics of corpus for getting information about the frequency of words that were used in the forums. The content in the forums was expected to be manipulated by religion and ideology. In the comparison between Qawem and Montada, it was found that Qawem contained more words related to radical ideology than Montana. In the results of the sentiment analysis of postings as percentages show that the Montada forum has less negative postings than the Qawem forum. In particular, the radical affect is quite strong in the communication found in the Qawem forum. VI. CONCLUSION In this research we have presented an analysis of two web forums, Montada and Qawem. The approach of model building and the results were explained. Overall, the results show that Qawem has more radical content than Montada. For future work, a comparative human evaluation can take place. We will ask people to rate sentences and see how their opinions on a rating scale compare to those of the model.  REFERENCES [1] J. Glaser, J. Dixit, and D. P. Green, "Studying Hate Crime with the Internet: What Makes Racists Advocate Racial Violence?" Journal of Social Issues, vol. 58, pp. 177-193, 2002. [2] R. Tong, "An operational system for detecting and tracking opinions on-line," presented at the In Proc of SIGR Workshop on Operational Text Classification, New Orleans, Louisiana, 2001. [3] HM Government. (2006). Terrorism Act 2006. Available: www.legislation.gov.uk/ukpga/2006/11/section/1 [4]  J. C. Paye and J. H. Membrez, Global war on liberty: Telos Press Pub., 2007. [5] E. Parker, "Implementation of the UK Terrorism Act 2006 - The Relationship between Counterterrorism Law, Free Speech, and the Muslim Community in the United Kingdom versus the United States," Emory International Law Review 21 Emory Int'l L. Rev., pp. 711-758, 2007. [6] G. Morgan, "Government to block terrorist web sites," in computing.co.uk, ed, 2011. [7] P. Nessera, "Ideologies of Jihad in Europe," Terrorism and Political Violence, vol. 23, pp. 173-200, 2011. [8] S. Das and M. Chen, "Yahoo! for Amazon: Extracting market sentiment from stock message boards," presented at the Proceedings of the Asia Pacific Finance Association Annual Conference APFA, 2001. [9] R. Tong, "An operational system for detecting and tracking opinions on-line," presented at the In Proc of SIGR Workshop on Operational Text Classification, New Orleans, Louisiana, 2001. [10] B. Pang and L. Lee, "Opinion Mining and Sentiment Analysis," Found. Trends Inf. Retr., vol. 2, pp. 1-135, 2008. [11] K. Dave, S. Lawrence, and D. M. Pennock, "Mining the peanut gallery: opinion extraction and semantic classification of product reviews," presented at the Proceedings of the 12th International Conference on the World Wide Web, Budapest, Hungary, 2003. [12] A. Bermingham, M. Conway, L. McInerney, N. O'Hare, and A. F. Smeaton, "Combining Social Network Analysis and Sentiment Analysis to Explore the Potential for Online Radicalisation," presented at the Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, 2009. [13] A. Khan and B. Baharudin, "Sentiment classification using sentence-level semantic orientation of opinion terms from blogs," presented at the National Postgraduate Conference (NPC), 2011. [14] K. Denecke, "Using SentiWordNet for multilingual sentiment analysis," presented at the Data Engineering Workshop, 2008. ICDEW 2008. IEEE 24th International Conference, 2008. [15] A. Hamouda and M. Rohaim, "Reviews Classification Using SentiWordNet Lexicon," The Online Journal on Computer Science and Information Technology (OJCSIT) vol. 2, pp. 120-123, 2011. [16] A. Neviarouskaya, H. Prendinger, and M. Ishizuka, "Textual Affect Sensing for Sociable and Expressive Online Communication," presented at the Proceedings of the 2nd International Conference on Affective Computing and Intelligent Interaction, Lisbon, Portugal, 2007.     

An operational system for detecting and tracking opinions online,&quot; presented at the In

Combining Social Network Analysis and Sentiment Analysis to Explore the Potential for Online Radicalisation,&quot; presented at the

Global war on liberty: Telos Press Pub.,

Government to block terrorist web sites,&quot;

Ideologies of Jihad in Europe,&quot;

Implementation of the UK Terrorism Act

Mining the peanut gallery: opinion extraction and semantic classification of product reviews,&quot; presented at the

Opinion Mining and Sentiment Analysis,&quot;

Reviews Classification Using SentiWordNet Lexicon,&quot;

Sentiment classification using sentencelevel semantic orientation of opinion terms from blogs,&quot; presented at the National Postgraduate Conference (NPC),

Studying Hate Crime with the Internet: What Makes Racists Advocate Racial Violence?&quot;

Terrorism Act

Textual Affect Sensing for Sociable and Expressive Online Communication,&quot; presented at the

Using SentiWordNet for multilingual sentiment analysis,&quot; presented at the Data Engineering Workshop,

Yahoo! for Amazon: Extracting market sentiment from stock message boards,&quot; presented at the

http://nrl.northumbria.ac.uk/id/eprint/13075/1/1_____Chalothorn_Ellman_SKIMA_2012.pdf

Using SentiWordNet and Sentiment Analysis for Detecting Radical Content on Web Forums

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