Discovering user attitudes of business in Twitter language feed

Other Title(s)

فهم و اكتشاف توجهات المستخدمين العرب لتويتر و ربطها مع الأونتولوجي

Dissertant

Abd al-Nabi, Ibrahim Muhammad

Thesis advisor

Tartir, Samir

Comitee Members

Hanna, Samir S.
Hadi, Wail

University

Philadelphia University

Faculty

Faculty of Information Technology

Department

Department of Computer Science

University Country

Jordan

Degree

Master

Degree Date

2013

English Abstract

-Research done on Arabic sentiment analysis is considered very limited almost in its early steps compared to other languages like English whether at business level or social level.

Twitter now can be considered as an information network instead of a social network; this is because Twitter is a platform for shared experiences and it is a very human network .Twitter, a micro-blogging service, has emerged as a new medium in spotlight through recent happenings.

Sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document.

The attitude may be his or her judgment or evaluation.

Thesis problems are discovering user attitudes and business insights from Arabic Twitter feed and Enhancing Arabic Natural Language Processing using novel Semantic Web techniques The Motivation for our work is Social media have swept into every industry and business function and are now an important factor of production, The primary goal of our work is to help companies make better business decisions by enabling semantic web and data mining.

We proposes a new model to discover Arabic business insights from tweets that may affect the decision making process, This model classifies Arabic tweets into positive and negative and tries to enhance the classification precision of tweets by creating Arabic Sentiment Ontology (ASO) for first time and also building our ASO aggregate weights algorithm.

Our Solution will be a starting point for any research that focus on Arabic sentiment analysis and extract information from Arabic social media.

Main Subjects

Information Technology and Computer Science

Topics

• Social networking Web sites
• Jordan
• Internet
• Social aspects

No. of Pages

50

Table of Contents

• APA
• MLA
• AMA

Language

English

Data Type

Arab Theses

Record ID

BIM-546125