Diab Abuaiadah, Dileep Rajendran, Mustafa Jarrar: Clustering Arabic Tweets for Sentiment Analysis. Proceedings of the 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications. IEEE Computer Society. DOI 10.1109/AICCSA.2017.162

1. 14th International Conference on Computer Systems and Application (AICCSA’17), 30 October 2017 1
Clustering Arabic Tweets
for Sentiment Analysis
Diab Abuaiadah
Diab.Abuaiadah@Wintec.ac.nz
Waikato Institute of Technology
New Zealand
Dileep Rajendran
Dileep.Rajendran@Wintec.ac.nz
Waikato Institute of Technology
New Zealand
Mustafa Jarrar
mjarrar@birzeit.edu
Birzeit University
Palestine

2. 14th International Conference on Computer Systems and Application (AICCSA’17), 30 October 2017 2
This talk is based on:
Diab Abuaiadah, Dileep Rajendran, Mustafa Jarrar:
Clustering Arabic Tweets for Sentiment Analysis.
Proceedings of the 2017 IEEE/ACS 14th International
Conference on Computer Systems and Applications.
IEEE Computer Society. DOI
10.1109/AICCSA.2017.162
http://www.jarrar.info/publications/ARJ17.pdf

3. 14th International Conference on Computer Systems and Application (AICCSA’17), 30 October 2017 3
Characteristics and challenges of Arabic Tweets
 Short text: “Less statistical data” implies poor result, thus less
effective information retrieval algorithms.
 Dialectal language: vast geographical area which includes the
Middle east and north Africa.
 Informal language: does not follow language grammar and
rules.
 Different regions have different dialects.

4. 14th International Conference on Computer Systems and Application (AICCSA’17), 30 October 2017 4
Importance of extracting knowledge
from Arabic tweets
 Social and political sentiments: measures the popularity of
political and/or social policies.
 Marketing products : extracting positive and negative opinion,
discovering recent trends and detecting product popularity.
 Sarcastic and non-Sarcastic tweets: also important for
detecting sentiments.

5. 14th International Conference on Computer Systems and Application (AICCSA’17), 30 October 2017 5
What preprocessing steps are needed?
 Removing stop words!
 In many applications removing stop words may improve the performance
 In sentiment analysis removing stop might result in loosing valuable
information. For example: keeping negative terms such as “no” can be
useful to indicate negative sentiments
 Lemmatization!
 More challenging with informal and short text
 May produce high errors ratio
 Stemming!
 Also has high error ratio for informal short text

6. 14th International Conference on Computer Systems and Application (AICCSA’17), 30 October 2017 6
Similarity Functions
 Most information retrieval algorithms employ a similarity (or
distance) function to measure the similarity between :
 two documents (Tweets)
 or a document (Tweet) and a group of documents (Tweets)
 There are five commonly used similarity functions in natural
language processing applications: Euclidian, Cosine, Pearson,
Jaccard and averaged Kullback-Leibler divergence (KLD)
The goal of this paper is to evaluate and compare these
functions in clustering Arabic tweets for sentiment analysis

7. 14th International Conference on Computer Systems and Application (AICCSA’17), 30 October 2017 7
The similarity functions

8. 14th International Conference on Computer Systems and Application (AICCSA’17), 30 October 2017 8
Our Datasets and Preprocessing
 An open dataset with 2000 tweets (Jordan dialect)
N. Abdulla, N. Mahyoub, M. Shehab and N. Al-Ayyoub, "Arabic sentiment analysis:
Corpus-based and lexicon- based," in In Proceedings of The IEEE conference on Applied
Electrical Engineering and Computing Technologies (AEECT)., 2013.
 Other datasets were available but the labeling was
immature at the time of writing the paper.
 Preprocessing includes removing stop words, Light10
and Khoja stemmers.
 Clustering using the K-Means (and Bisect K-Means)
algorithms.

9. 14th International Conference on Computer Systems and Application (AICCSA’17), 30 October 2017 9
Implementation
 Khoja’s stemmer: the Java code was taken from the author
home page. http://zeus.cs.pacificu.edu/shereen/research.htm
 Light10 and removing stop words: in-house Java code. The
implementation is straightforward as it simply strip off a
predefined fixed set of prefixes and suffixes.
 Clustering and Bisect clustering: in-house Java code using
Hashtable to represent TF-IDF of each document.
(TF: Term Frequency, IDF: Inverse Document Frequency)
 Purities and entropies: in-house Java code.
To measure the quality of the algorithm’s results

10. 14th International Conference on Computer Systems and Application (AICCSA’17), 30 October 2017 10
Testing the code
 The code for the similarity functions, K-Means clustering,
purity and entropy measures were tested by applying it to a
well-known dataset
20 Newsgroup dataset: http://csmining.org/index.php/id-20-newsgroups.html
 The purities and entropies results (when applying the K-
Means) is comparable to that of Huang (2008):
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.332.4480&rep=rep1&type=pdf

11. 14th International Conference on Computer Systems and Application (AICCSA’17), 30 October 2017 11
Purities: K-means algorithm
Raw: no pre-processing
NoSW: Only removed stop words (the known list)
Light10: Removed stop words + Light10
Root: Removed stop words + Khoja’s
- Each test was repeated 50 times, and the average is used.
- The Euclidian was bad

12. 14th International Conference on Computer Systems and Application (AICCSA’17), 30 October 2017 12
Purities for K-Means
 KLD clearly outperformed the Cosine similarity function
 KLD outperformed the Cosine similarity function
 This is of interest as the Cosine similarity function is commonly used in
many products and published papers.
 The Cosine function outperforms KLD for normal-sized texts, but for
tweets, as our experiment is showing.
 Kohja's stemmer (Root) clearly outperforms the Light10
stemmer
 However, Light10 stemmer outperforms Kohja’s stemmer for normal-
sized texts

13. 14th International Conference on Computer Systems and Application (AICCSA’17), 30 October 2017 13
Purities: optimized K-Means
 The optimized K-Means (Bisect K-Means) improves the results
compared to that of K-Means (68% to 76%).
 KLD and Kohja’s stemmer retained their superiority compared to
other similarity functions and other processing.

14. 14th International Conference on Computer Systems and Application (AICCSA’17), 30 October 2017 14
Future Research
 Lemmatization: could be more effective than stemmers
 Are other clustering algorithms more effective than K-
Means? Hierarchical algorithms produce better clustering
results but have higher running time
 N-gram preprocessing is an interesting approach that could
lead to higher accuracies

15. 14th International Conference on Computer Systems and Application (AICCSA’17), 30 October 2017 15
Thank you
Questions?
Questions?

16. 14th International Conference on Computer Systems and Application (AICCSA’17), 30 October 2017 16
References
[1] S. Khoja, 2016. [Online]. Available: http://zeus.cs.pacificu.edu/shereen/research.htm. [Accessed 01 August 2016].
[2] R. M. Duwairi, R. Marji, N. Sha'ban and S. Rushaidat, "Sentiment Analysis in Arabic Tweets,”. Proceedings of ICICS 2014.
[3] E. Zangerle, G. Wolfgang and G. Specht, "On the impact of text similarity functions on hashtag recommendations in microblogging environments," Social
Network Analysis and Mining, vol. 3, no. 4, pp. 889-898, 2013.
[4] S. R. El-Beltagy and A. Ali, "Open Issues in the Sentiment Analysis of Arabic," in IIT, 2013.
[5] H. Froud, R. Benslimane, A. Lachkar and S. A. & Ouatik, "Stemming and similarity measures for Arabic Documents Clustering," in I/V IEE ISVC, 2010.
[6] H. Froud, A. Lachkar and S. Ouatik, "Arabic text summarization based on latent semantic analysis to enhance arabic documents clustering," International
Journal of Data Mining & Knowledge Management Process (IJDKP), 2013.
[7] D. Abuaidah, "Using Bisect K-Means Clustering Technique in the Analysis of Arabic Documents," ACM Transactions on Asian and Low-Resource Language
Information Processing, vol. 15, no. 3, p. 17, 2016.
[8] Q. Bsoul and M. Mohd, "Effect of ISRI stemming on similarity measure for Arabic document clustering," in Asia Information Retrieval Symposium, 2011.
[9] O. Ghanem and W. M. Ashour, "Stemming Effectiveness in Clustering of Arabic Documents," Journal of Computer Applications, vol. 5, no. 49, 2012.
[10] L. Larkey, L. Ballesteros and M. Connell, "Improving Stemming for Arabic Information Retrieval: Light Stemming and Co-occurrence Analysis," ACM, 2002.
[11] N. Sandhya, Y. S. Lalitha, V. Sowmya, A. D. K.. and D. A. Govardhan, "Analysis of Stemming Algorithm for Text Clustering," vol. 8, no. 5, 2011.
[12] A. Shoukry and A. Rafea, "Sentence-Level Arabic Sentiment Analysis," in International Conference on Collaboration Technologies and Systems, 2012.
[13] B. Pang and L. Lillian, "Opinion mining and sentiment analysis," Foundations and trends in information retrieval, vol. 2, no. 1-2, pp. 1-135, 2008.
[14] M. Althobaiti, U. Kruschwitz and M. Poesio, "Combining Minimally-supervised Methods for Arabic Named Entity Recognition," Transactions of the
Association for Computational Linguistics, vol. 3, pp. 243-255, 2015.
[15] M. Efron, "Information search and retrieval in microblogs," Information search and retrieval in microblogs." Journal of the American Society for Information
Science and Technology, vol. 62, no. 6, pp. 996-1008, 2011.
[16] J. Akshay, X. Song, T. Finin and B. Tseng, "Why we twitter: understanding microblogging usage and communities.," in In Proceedings of the 9th WebKDD
and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, 2007.
[17] G. Salton and C. Buckley, "Term weighting approaches in automatic text retrieval," Information Processing and Management, vol. 24, no. 5. 1988.
[18] G. Salton, Automatic text processing: the transformation, analysis, and retrieval of information by computer, Boston: Addison-Wesley Longman Co., 1989.
[19] A. Go, R. Bhayani and L. Huang, "Twitter Sentiment Classification using Distant Supervision," CS224N Project Report, Stanford, 2009.
[20] A. K. Jose, N. Bhatia and S. Krishna, "TWITTER SENTIMENT ANALYSIS," National Institute of Technology, Calicut, Monsoon, 2010.
[21] A. Huang, "Similarity measures for text document clustering," in Proceedings of the sixth new zealand computer science research student conference
(NZCSRSC2008), Christchurch, New Zealand, 2008.
[22] M. Steinbach, G. Karypis and V. Kumar, "A Comparison of Document Clustering Techniques," in KDD Workshop on Text Mining, 2000.
[23] S. Khoja and R. Garside, "Stemming Arabic text," Lancaster University, Lancaster, 1999.
[24] N. Abdulla, N. Mahyoub, M. Shehab and N. Al-Ayyoub, "Arabic sentiment analysis: Corpus-based and lexicon-based," in In Proceedings of The IEEE
conference on Applied Electrical Engineering and Computing Technologies (AEECT)., 2013.
[25] K. Darwish, W. Magdy and A. Mourad, "Language processing for arabic microblog retrieval," in Proceedings of the 21st ACM international conference on
Information and knowledge management, 2012.
[26] A. Newsri, "Effective Retrieval Techniques for Arabic Text," Melbourne, Australia, 2008.
[27] E. Al-Shammari and J. Lin, "Towards an Error-Free Arabic Stemming," in Proceedings of the 2nd ACM workshop on Improving non english web searching
(iNEWS '08), New York, NY, USA, 2008A.
[28] E. Al-Shammari and J. Lin, "A novel Arabic lemmatization algorithm," in Proceedings of the second workshop on Analytics for noisy unstructured text data.
[29] A. Jain, "Data clustering: 50 years beyond K-means," Pattern Recognition Letters, vol. 31, p. 651–666, 2010.
[30] R. Kashef and M. S. Kamel, "Enhanced bisecting k-means clustering using intermediate cooperation," Pattern Recognition, vol. 42, no. 11, 2009.
[31] C. Manning, P. Raghavan and H. Schütze, Introduction to information retrieval, vol. 1, Cambridge: Cambridge university press, 2008.