Authors

Tarek Elghazaly

Amal Mahmoud

Hesham A. Hefny

Political Sentiment Analysis Using

Twitter Data

Computer Science Department, Institute of Statistical Studies and

Research (ISSR), Cairo University, Egypt

ICC '16, March 22-23, 2016, Cambridge, United Kingdom © 2016 ACM. ISBN978-1-

4503-4063-2/16/$15.00

DOI: http://dx.doi.org/10.1145/2896387.2896396

OVERVIEW

1. INTRODUCTION

3. METHODOLOGY

4. RESULTS AND EVALUATION

5.CONCLUSION AND FUTURE WORK

2. RELATED WORK

There is a remarkable growth in the usage of social networks, such

as Facebook and Twitter. Users from different cultures and

backgrounds post large volumes of textual comments reflecting their

opinion in different aspect of life and make them available to

everyone. In particular we study the case of Twitter and focus on

presidential elections in Egypt 2012.

Sentiment analysis is the automated mining of attitudes,

opinions, and emotions from text, speech, and database

sources. Sentiment analysis involves classifying opinions in text

into categories like “positive” or “negative” or “neutral”.

INTRODUCTION

Sentiment analysis of Arabic social media is challenging task for

many reasons such as :

1. Arabic is not a case-sensitive language.

2. Arabic has some variants in spelling and typographic forms,

Creation of new expressions the usage of which indicates high

subjectivity for example” (funky)”روش is used as a positive

reference. Also“االستبن”(spare tire) is used as negative reference

used for former president Mohammed Morsi during the 2012

Presidential elections.

3. Arabic texts have different sorts of ambiguities (different meanings).

For example, (Ramadan)”رمضان“ in Arabic may be used as a

person name, month.

4. a form of speech act where a person says something positive

while (s)he really means something negative or vice versa.

INTRODUCTION

Mining Arabic Business Reviews (Elhawary, M., Elfeky, M. 2010)

Presents a system to extract business Arabic reviews, and then it

analyzed these collected reviews to id identify their polarity (positive,

negative or neutral). And exhibits the general opinion of the Arab public

about different products and services.

Automatic Arabic Document Categorization Based on the Naïve Bayes

Algorithm(El-Kourdi, M., Bensaid, A., and Rachidi, T . 2004) used Naïve

Bayes algorithm for categorizing Arabic text documents to one of five

pre-defined categories, Cross validation experiments are used to

evaluate the Naïve Bayes categorizer.

Opinion corpus for Arabic Rushdi-Saleh, M., Martín-Valdivia, M., Ureña-

López, L., and Perea-Ortega, J. 2011) uses machine learning classifiers

by using both Arabic and English corpora. They employ two machine

learning classifiers namely, (SVMs) and (NB) classifiers. The results

obtained show that SVMs outperform the NB classifier and also there is

no a big difference between using the term frequency (TF) and the term

frequency-inverse document frequency (TF-IDF) for weighting methods.

RELATED WORK

The methodology using for Building Machine Learning classifies consists of 3 steps

1. Corpus Collection and Preparation

2. Pre-processing

3. Text Classification

Then convert the file to ARFF which deals with Weka program. WEKA provides a

large collection of machine learning algorithms for data pre-processing,

classification, clustering, association rules, and visualization, which can be

invoked through a common Graphical User Interface.

METHODOLOGIES

Corpus Collection and Preparation

The total corpus size is 18278 tweets. We have

annotated 18278 tweets consisting of 11910 positive,

6368 related to pinion expressed in Arabic from different

domains:” علىخالد ” -(Khaled Ali),” موسىعمرو ”-(Amr

Mousa),” شفيقاحمد ”-(Ahmed Shafik),” مرسىمحمد" -

(Mohammed Morsi), "صباحىحمدين" Hamden Sabahy)-

( "الفتوحابو" -(Abu Alftouh)”.We define a sentiment as

positive or negative opinion, each data instance (Tweet)

annotated to positive or negative.

Pre-processing

1. Tokenization

2. Normalization

3. Stop words removal

4. Stemming

5. Term weighting

6. N-Grams

Text Classification

This section covers two existing approaches to text classification: SVM, and NB.

Support Vector Machine (SVM): This classifier can recognized by a separating hyper

plane To put it simply, the output of the algorithm is the optimal hyper plane that

put new examples in categories after receiving labelled predefined training data

Naïve Bayes (NB): is an effective classification algorithm which is widely used for

sentiment analysis and document classification. As a probabilistic model, the

Naïve Bayes classifier makes the use of the joint probabilities of terms and their

categories for the sake of figuring out the probabilities of categories given as a

test data.

METHODOLOGIES

represents the precision, recall and F-measure for each category and the

average values for all categories for the SVM classifier

METHODOLOGIES

precision Recall F-measure Class

0.932 0.953 0.942 أحمد شفيق

0.783 0.885 0.831 محمد مرسى

0.000 0.000 0.000 عمرو موسى

0.970 0.935 0.952 خالد على

0.950 0.743 0.834 حمدين صباحى

0.914 0.917 0.916 ابو الفتوح

0.862 0.884 0.871 Weighted average

represents the precision, recall and F-measure for each category and

the average values for all categories for the NB classifier

precision Recall F-measure Class

0.976 0.920 0.947 أحمد شفيق

0.865 0.951 0.906 محمد مرسى

0.844 0.889 0.866 عمرو موسى

0.912 0.941 0.926 خالد على

0.848 0.871 0.859 حمدين صباحى

0.953 0.886 0.918 ابو الفتوح

0.925 0.921 0.922 Weighted average

The evaluation is based on two popular machine learning algorithms (NB, SVM)

using unigram as feature, and using 10-fold cross validation method for testing.

The evaluation we used precision, recall and F-measure to evaluate these

approaches.

RESULTS AND EVALUATION

F-measure=2∗Recall∗Precision

(Recall+Precision)Precision=

TP

TP+FPRecall=

TP

TP+FNAccuracy=

TP+TN

TP+FP+TN+FN

Evaluating Result

Classification Type

Precision RecallF-

Measure

SVM 0.862 0.884 0.871

NB 0.925 0.921 0.922

Time taken to build the models in

Minuets

Classification Type

Time

SVM 161.03

NB 30.59

This study aims to compare between two classification techniques SVM and NB

using Arabic tweets which categorized the Arabic documents into six domains :”

علىخالد ” -(Khaled Ali), ” موسىعمرو ” -(Amr Mousa), ” شفيقاحمد ”-(Ahmed Shafik), ”

"مرسىمحمد -(Mohammed Morsi), صباحى""حمدين -(Hamden Sabahy), "الفتوحابو" -(Abu

Alftouh).The bases of our comparison of the SVM and NB are the most popular

text evaluation measures (F-measure, Recall, and Precision). TF-IDF is used as

the weighting scheme and cosine measure used to calculate the similarity

of each document to be classified with training documents. The comparison is

based on two main aspects for the selected classifiers, accuracy and time. In

terms of accuracy, results show that the Naïve Bayes is a popular technique

for this application because it is very fast and quite accurate.

Future work:

we aim to compare the results obtained from these classifiers with other

classifiers. In this study we used light stemmer we aim to use Khoja stemmer and

compare the result; also we used unigram we aim to compare the result between

unigram, bigram and trigram.

CONCLUSION AND FUTURE WORK