sentiment analysis of arabic,a survey

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

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

• 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

OUTLINE:



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).

OUTLINE:



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).

OUTLINE:



1. SAA CATEGORIES:

OUTLINE:



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)

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

OUTLINE:



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

RESULT:


(CONT)

movement words & metaphorical words from Middle East and NorthAfrica Financial Network (MENA-FN) corpus

RESULT:


(CONT)

Local grammar in Arabic text


(CONT)

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

OUTLINE:



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 .

under sampling method:Propose FOUR different techniques• Remove Similar (RS)• Remove Farthest (RF)• Remove by Clustering (RC).• Random Removable (RR).


(CONT)

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.


(CONT)

4) Performance measure:

CONFUSION MATRIX

• g-performance:


(CONT)

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


(CONT)RESULT:

THANK YOU

sentiment analysis of arabic,a survey

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