sentiment analysis of arabic,a survey
DESCRIPTION
TRANSCRIPT
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Sentiment Analysis of Arabic: A Survey
Sara Mohammed AL-Kharji AND
Anfal Abdullah AL-TuwaimSupervised by:Dr. Amal Alsaif
Imam Mohammed Ibn Saud Islamic UniversityCollege of Computer and Information SciencesNatural Languages Processing (CS465)Semester 2, 2013
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OUTLINE:
• Introduction.•Arabic.• Sentiment Analysis Systems and Methods for
Arabic:• SAA categories.• Automatic Classification.• Automatically extracting sentiments from financial texts.• Unbalanced Sentiment Classification in an Arabic context
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OUTLINE:
• Introduction.•Arabic.• Sentiment Analysis Systems and Methods for
Arabic:• SAA categories.• Automatic Classification.• Automatically extracting sentiments from financial texts.• Unbalanced Sentiment Classification in an Arabic context
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• 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.
INTRODUCTION
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OUTLINE:
• Introduction.•Arabic.• Sentiment Analysis Systems and Methods for
Arabic:• SAA categories.• Automatic Classification.• Automatically extracting sentiments from financial texts.• Unbalanced Sentiment Classification in an Arabic context
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ARABIC
• 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).
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OUTLINE:
• Introduction.•Arabic.• Sentiment Analysis Systems and Methods for
Arabic:• SAA categories.• Automatic Classification.• Automatically extracting sentiments from financial texts.• Unbalanced Sentiment Classification in an Arabic context
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SENTIMENT ANALYSIS SYSTEMS AND METHODS FOR ARABIC:
• 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).
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OUTLINE:
• Introduction.•Arabic.• Sentiment Analysis Systems and Methods for
Arabic:• SAA categories.• Automatic Classification.• Automatically extracting sentiments from financial texts.• Unbalanced Sentiment Classification in an Arabic context
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1. SAA CATEGORIES:
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OUTLINE:
• Introduction.•Arabic.• Sentiment Analysis Systems and Methods for
Arabic:• SAA categories.• Automatic Classification.• Automatically extracting sentiments from financial texts.• Unbalanced Sentiment Classification in an Arabic context
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2. AUTOMATIC CLASSIFICATION:
• 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)
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2. AUTOMATIC CLASSIFICATION: (CONT)
• 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
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OUTLINE:
• Introduction.•Arabic.• Sentiment Analysis Systems and Methods for
Arabic:• SAA categories.• Automatic Classification.• Automatically extracting sentiments from financial texts.• Unbalanced Sentiment Classification in an Arabic context
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3. AUTOMATICALLY EXTRACTING SENTIMENTS FROM FINANCIAL TEXTS:
(CONT)
• 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
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RESULT:
3. AUTOMATICALLY EXTRACTING SENTIMENTS FROM FINANCIAL TEXTS:
(CONT)
movement words & metaphorical words from Middle East and NorthAfrica Financial Network (MENA-FN) corpus
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RESULT:
3. AUTOMATICALLY EXTRACTING SENTIMENTS FROM FINANCIAL TEXTS:
(CONT)
Local grammar in Arabic text
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3. AUTOMATICALLY EXTRACTING SENTIMENTS FROM FINANCIAL TEXTS:
(CONT)
Prototypes of Ara-SATISFI “Arabic Sentiment and Time Series: Financial Analysis System”
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OUTLINE:
• Introduction.•Arabic.• Sentiment Analysis Systems and Methods for
Arabic:• SAA categories.• Automatic Classification.• Automatically extracting sentiments from financial texts.• Unbalanced Sentiment Classification in an Arabic context
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4. UNBALANCED SENTIMENT CLASSIFICATION IN AN ARABIC CONTEXT
(CONT)
• 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 .
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under sampling method:Propose FOUR different techniques• Remove Similar (RS)• Remove Farthest (RF)• Remove by Clustering (RC).• Random Removable (RR).
4. UNBALANCED SENTIMENT CLASSIFICATION IN AN ARABIC CONTEXT
(CONT)
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EXPERIMENTS1) Preprocessing2) Classification and algorithmsThe 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.
4. UNBALANCED SENTIMENT CLASSIFICATION IN AN ARABIC CONTEXT
(CONT)
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4) Performance measure:
CONFUSION MATRIX
• g-performance:
4. UNBALANCED SENTIMENT CLASSIFICATION IN AN ARABIC CONTEXT
(CONT)
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• Have used two standard classifiers: Naïve Bayes (NB) AND Support Vector Machines (SVM).
4. UNBALANCED SENTIMENT CLASSIFICATION IN AN ARABIC CONTEXT
(CONT)RESULT:
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THANK YOU