1. Sentiment Analysis of Arabic: A
Survey
Sara Mohammed AL-Kharji
AND
Anfal Abdullah AL-Tuwaim
Supervised by:
Dr. Amal Alsaif
Imam Mohammed Ibn Saud Islamic University
College of Computer and Information Sciences
Natural Languages Processing (CS465)
Semester 2, 2013

2. OUTLINE:

3. OUTLINE:

4. • Sentiment analysis is the field of study that
analyzes people's opinions, sentiments,
evaluations, attitudes, and emotions from
written language.
• Most of the systems built for sentiment
analysis are tailored for the English language,
but there are very few resources for other
languages.

5. OUTLINE:

6. • Official language of 22 countries, Arabic is spoken
by more than 300 million people
• The fastest-growing language on the web
• Arabic is a Semitic language and consists of many
different regional dialects
• Modern Standard Arabic (MSA)
• Arabic sentential forms are divided into two
types, nominal and verbal constructions . In the
verbal domain, Arabic has two word order
patterns (i.e., Subject-Verb- Object and Verb-
Subject-Object).

7. OUTLINE:

8. • Subjectivity process:
– Tokenization.
– Stemming.
– Stop Words elimination.
• Sentiment process:
(1) Objective (OBJ).
(2) Subjective-Positive (S-POS).
(3) Subjective-Negative (S-NEG).
(4) Subjective-Neutral (S-NEUT).

9. OUTLINE:

11. OUTLINE:

12. • Run experiments on gold-tokenized text from
PATB.
• Experiment with three different pre-
processing lemmatization configurations that
specifically target the stem words: (1) Surface;
(2) Lemma; and (3) Stem.
• It adopts a two-stage classification approach:
– (Subjectivity)
– (Sentiment)

13. • Use TreeBank (PATB), And dividing data into 80% for
5-fold cross validation and 20% for test.
• Subjectivity results on Stem+Morph+language independent features
• Sentiment results on Stem+Morph+language independent features

14. OUTLINE:

15. •Importance of sentiments analysis for financial
market.
•The sentiment words were selected comprised
movement words, rise/fall, and metaphorical
words like growth/decline.
•Local grammar

16. movement words & metaphorical words from Middle East and North
Africa Financial Network (MENA-FN) corpus

17. Local grammar in Arabic text

18. Prototypes of Ara-SATISFI “Arabic Sentiment and Time Series: Financial Analysis System”

19. OUTLINE:

20. •For most studies in SA, can note that the problem of
unbalanced data sets (UD) is not tackled.
•There are generally two approaches for UD.
- The first approach tends to modify the classifier
-The second approach deals with the modification of
the data set itself
•Two common methods, the modification of the data set.
- The first focuses on under sampling.
- The second deals with over-sampling .

21. Propose FOUR different techniques
• Remove Similar (RS)
• Remove Farthest (RF)
• Remove by Clustering (RC).
• Random Removable (RR).

22. EXPERIMENTS
1) Preprocessing
2) Classification and algorithms
The categories to consider are POSITIVE, NEGATIVE, OBJECTIVE and
NOT_ARABIC. POSITIVE
3)Validation method:
randomly split into two sets: a training set representing 75% of the
data set, and a test set representing 25% of the data set.

23. 4) Performance measure:
CONFUSION MATRIX
•g-performance:

24. • Have used two standard classifiers:
Naïve Bayes (NB) AND Support Vector Machines (SVM).