Clustering Arabic Tweets for Sentiment Analysis

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

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

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.

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.

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

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

The similarity functions

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.

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

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

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

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

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.

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

Thank you
Questions?
Questions?

References
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