حامد سعد عطيه الربيعي

 


      
 
الاسم الاول: 
حامد
اسم العائلة: 
الربيعي
الدرجة العلمية: 
دكتوراة
مجال الدراسة: 
العلوم و التقنية
المؤسسة التعليمية: 
Bedfordshire University

مجال التميز

تميز دراسي و بحثي

 

 

البحوث المنشورة

 

البحث (1):

 

عنوان البحث:

THE IMPORTANCE OF NEUTRAL CLASS IN SENTIMENT ANALYSIS OF ARABIC TWEETS

رابط إلى البحث:

Click here

تاريخ النشر:

02/04/2016

موجز عن البحث:

Product reviews are becoming increasingly useful. In this paper, Twitter has been chosen as a platform for opinion mining in trading strategy with Mubasher products, which is a leading stock analysis software provider in the Gulf region. This experiment proposes a model for sentiment analysis of Saudi Arabic (standard and Arabian Gulf dialect) tweets to extract feedback from Mubasher products. A hybrid of natural language processing and machine learning approaches on building models are used to classify tweets according to their sentiment polarity into one of the classes positive, negative and neutral. In addition, regarding to the comparison between SVM and Bayesian method, we have split the data into two independents subsets form different periods and the experiments were carried out for each subset respectively in order to distinction between positive and negative examples by using neutral training examples in learning facilitates. Similar result has been given.

 

 

المؤتمرات العلمية:

 

المؤتمر (1):

 

عنوان المؤتمر:

IEEE Seventh International Conference on Intelligent Computing and Information Systems, ICICIS15

تاريخ الإنعقاد:

12/12/2015

مكان الإنعقاد:

Cairo. Egypt

طبيعة المشاركة:

Paper presentation

عنوان المشاركة:

Analysis of the Relationship Between Saudi Twitter Posts and the Saudi Stock Market

ملخص المشاركة:

Sentiment analysis has become the heart of social media research and many studies have been applied to obtain users' opinion in fields such as electronic commerce and trade, management and regarding political figures. Social media has recently become a rich resource in mining user sentiments. Social opinion has been analysed using sentiment analysis and some studies show that sentiment analysis of news, documents, quarterly reports, and blogs can be used as part of trading strategies. In this paper, Twitter has been chosen as a platform for opinion mining in trading strategy with the Saudi stock market in order to carry out and illustrate the relationship between Saudi tweets (that is standard and Arabian Gulf dialects) and the Saudi market index. To the best of our knowledge, this is the first study performed on Saudi tweets and the Saudi stock market.

 

 

المؤتمر (2):

 

عنوان المؤتمر:

2016 International Conference on Industrial Informatics and Computer Systems

تاريخ الإنعقاد:

13/03/2016

مكان الإنعقاد:

Sharjah, United Arab Emirates

طبيعة المشاركة:

Paper presentation

عنوان المشاركة:

Identifying Mubasher Software Products through Sentiment Analysis of Arabic Tweets

ملخص المشاركة:

Social media has recently become a rich resource in mining user sentiments. In this paper, Twitter has been chosen as a platform for opinion mining in trading strategy with Mubasher products, which is a leading stock analysis software provider in the Gulf region. This experiment proposes a model for sentiment analysis of Saudi Arabic (standard and Arabian Gulf dialect) tweets to extract feedback from Mubasher products. A hybrid of natural language processing and machine learning approaches on building models are used to classify tweets according to their sentiment polarity into one of the classes positive, negative and neutral. Firstly, document’s Pre-processing are explored on the dataset. Secondly, Naive Bayes and Support Vector Machines (SVMs) are applied with different feature selection schemes like TF-IDF (Term Frequency–Inverse Document Frequency) and BTO (Binary-Term Occurrence). Thirdly, the proposed model for sentiment analysis is expanded to obtain the results for N-Grams term of tokens. Finally, human has labelled the data and this may involve some mistakes in the labelling process. At this moment, neutral class with generalization of our classification will take results to different classification accuracy.

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