Sentiment analysis of arabic,a survey

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Sentiment analysis of arabic,a survey

  1. 1. Sentiment Analysis of Arabic: ASurveySara Mohammed AL-KharjiANDAnfal Abdullah AL-TuwaimSupervised by:Dr. Amal AlsaifImam Mohammed Ibn Saud Islamic UniversityCollege of Computer and Information SciencesNatural Languages Processing (CS465)Semester 2, 2013
  2. 2. OUTLINE:
  3. 3. OUTLINE:
  4. 4. • Sentiment analysis is the field of study thatanalyzes peoples opinions, sentiments,evaluations, attitudes, and emotions fromwritten language.• Most of the systems built for sentimentanalysis are tailored for the English language,but there are very few resources for otherlanguages.
  5. 5. OUTLINE:
  6. 6. • Official language of 22 countries, Arabic is spokenby more than 300 million people• The fastest-growing language on the web• Arabic is a Semitic language and consists of manydifferent regional dialects• Modern Standard Arabic (MSA)• Arabic sentential forms are divided into twotypes, nominal and verbal constructions . In theverbal domain, Arabic has two word orderpatterns (i.e., Subject-Verb- Object and Verb-Subject-Object).
  7. 7. OUTLINE:
  8. 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. 9. OUTLINE:
  10. 10. OUTLINE:
  11. 11. • Run experiments on gold-tokenized text fromPATB.• Experiment with three different pre-processing lemmatization configurations thatspecifically target the stem words: (1) Surface;(2) Lemma; and (3) Stem.• It adopts a two-stage classification approach:– (Subjectivity)– (Sentiment)
  12. 12. • Use TreeBank (PATB), And dividing data into 80% for5-fold cross validation and 20% for test.• Subjectivity results on Stem+Morph+language independent features• Sentiment results on Stem+Morph+language independent features
  13. 13. OUTLINE:
  14. 14. •Importance of sentiments analysis for financialmarket.•The sentiment words were selected comprisedmovement words, rise/fall, and metaphoricalwords like growth/decline.•Local grammar
  15. 15. movement words & metaphorical words from Middle East and NorthAfrica Financial Network (MENA-FN) corpus
  16. 16. Local grammar in Arabic text
  17. 17. Prototypes of Ara-SATISFI “Arabic Sentiment and Time Series: Financial Analysis System”
  18. 18. OUTLINE:
  19. 19. •For most studies in SA, can note that the problem ofunbalanced 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 ofthe data set itself•Two common methods, the modification of the data set.- The first focuses on under sampling.- The second deals with over-sampling .
  20. 20. Propose FOUR different techniques• Remove Similar (RS)• Remove Farthest (RF)• Remove by Clustering (RC).• Random Removable (RR).
  21. 21. EXPERIMENTS1) Preprocessing2) Classification and algorithmsThe categories to consider are POSITIVE, NEGATIVE, OBJECTIVE andNOT_ARABIC. POSITIVE3)Validation method:randomly split into two sets: a training set representing 75% of thedata set, and a test set representing 25% of the data set.
  22. 22. 4) Performance measure:CONFUSION MATRIX•g-performance:
  23. 23. • Have used two standard classifiers:Naïve Bayes (NB) AND Support Vector Machines (SVM).

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