Sentiment analysis of micro blogs in education domain
Other Title(s)
نتيجة الآراء للمدونات في مجال التعليم
Dissertant
al-Haddad, Ala Ibrahim Zakariyya
Thesis advisor
Comitee Members
al-Halis, Ala Mustafa
Zaqqut, Ihab Salah al-Din
University
Islamic University
Faculty
Faculty of Information Technology
University Country
Palestine (Gaza Strip)
Degree
Master
Degree Date
2015
English Abstract
During the recent years, microblogging and social media have become very popular where millions of people post short text about different things.
Topics range from personal life and work, to current events, news, and interesting observations and political thoughts.
Education institutes become aware of the benefits engaging in such technology, and many instructors use social media in teaching courses they offer.
Courses adopting social media in the learning process allow students to discuss with each other and with their teacher different topics and express their opinions on various aspects of these topics.
The huge amount and variety of opinions generated out of these discussions create new opportunities for assessing teaching courses.
Manual methods for analyzing opinions in these huge amount of data are infeasible.
Sentiment analysis is a research field that focuses on automatically identifying the subjectivity and the polarity (e.g.
positive or negative) of a given text on an entity or a topic.
It is a classification problem, where learning algorithms are used.
Most of previous works focus on using supervised algorithms, however such algorithms are very expensive since we need to manually annotate a large amount of data for training the classifiers, in addition it is domain dependent (e.g.
products, movies, politics, etc.).
Besides, certain characteristics of social media content introduce challenges in their analysis.
Informal English blended with abbreviations, slangs and context specific terms; lacking in sufficient context and regularities and delivered with an indifferent approaches to grammar and spelling, all at the top of these characteristics.
Most of previous works on sentiment analysis tackle domains such as economic, products, movie reviews, and political domain.
There is a paucity of literature in the education domain.
Our research is a contribution to this field.
In particular, we propose a sentiment analysis prototype for microblogs posted in learning activities.
The prototype automatically classifies microblogs of learning activities into positive and negative with less costs in terms of learning requirements.
Our approach aims to achieve this objective using a novel combination of features extraction and engineering methods, and using a semi-supervised sentiment classification model based on label propagation algorithm.
The results returned of the experiments we conducted to evaluate the model were competitive to existing works.
The F-measures of our approach using different datasets has an average value of u 80%.
Keywords: Semi-supervise, Opinion mining, Sentiment Analysis, Microblogs.
Main Subjects
Information Technology and Computer Science
Topics
No. of Pages
68
Table of Contents
Table of contents.
Abstract.
Chapter One : Introduction.
Chapter Two : State of the art.
Chapter Three : Approach and methodology.
Chapter Four : System technical implementation.
Chapter Five : System experiments and evaluation.
Chapter Six : Conclusion and future work.
References.
American Psychological Association (APA)
al-Haddad, Ala Ibrahim Zakariyya. (2015). Sentiment analysis of micro blogs in education domain. (Master's theses Theses and Dissertations Master). Islamic University, Palestine (Gaza Strip)
https://search.emarefa.net/detail/BIM-610611
Modern Language Association (MLA)
al-Haddad, Ala Ibrahim Zakariyya. Sentiment analysis of micro blogs in education domain. (Master's theses Theses and Dissertations Master). Islamic University. (2015).
https://search.emarefa.net/detail/BIM-610611
American Medical Association (AMA)
al-Haddad, Ala Ibrahim Zakariyya. (2015). Sentiment analysis of micro blogs in education domain. (Master's theses Theses and Dissertations Master). Islamic University, Palestine (Gaza Strip)
https://search.emarefa.net/detail/BIM-610611
Language
English
Data Type
Arab Theses
Record ID
BIM-610611