JavaScript is disabled for your browser. Some features of this site may not work without it.
dc.contributor.author
Prasad, Kartika
en
dc.date.accessioned
2015-06-23T15:08:17Z
dc.date.available
2015-09-27T05:58:32Z
dc.date.issued
2015-06-23
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/500
dc.rights
Default License
dc.title
Automatic Ontology Generation for Sentiment Analysis from Twitter
en
heal.type
masterThesis
heal.keyword
Mass media and public opinion
en
heal.keyword
Internet--Social aspects
en
heal.keyword
Software engineering
en
heal.keyword
Computer-aided engineering
en
heal.keyword
Twitter--Social aspects
en
heal.keyword
Dissertations, Academic
en
heal.language
en
heal.access
free
el
heal.license
http://creativecommons.org/licenses/by-nc/4.0
heal.recordProvider
School of Science and Technology, MSc in Information & Communication Technology Systems
heal.publicationDate
2013-11
heal.bibliographicCitation
Prasad Kartika, 2013, Automatic ontology generation for sentiment analysis from twitter ,Master's Dissertation, International Hellenic University
en
heal.abstract
This thesis was written as a part of the MSc in ICT Systems at the International Hellenic University.
With the rising importance of knowledge interchange, along with the emergence of Web 2.0 an important problem has been introduced. This problem is namely how social content can be annotated with relevant semantic information, if this problem is solved in a distributed manner it will allow for the analysis of user made content to be more easily made in an automatic fashion. The analysis of social interactions within communities begins with search and without any further knowledge about the domain on which is being searched the analysis is limited.
In the thesis a methodology is defined for efficient and accurate sentiment analysis across the social networking platform Twitter. To add sentiment to the searching process ontologies are automatically (or semi-automatically) created in an object-attribute pattern. The methodology consists of three steps. (i) Create a domain ontology; (ii) Sentiment analysis on Tweets; (iii) Quantitative analysis on outputted sentiment scores.
The primary intention was to improve how sentiment analysis on Twitter will be conducted in a practical sense. This is achieved by allowing the inclusion of more data in searching and more relevant search into the Twitter API, due the use of ontologies.
Furthermore the thesis identifies a number of promising areas for future work. Finally, it gives a comprehensive overview of related, similar and subsumed approaches.
I would like to express my sincere thanks to Dr. E. Kontopoulos, my supervisor, for hi encouragement and support during my research. He always remained approachable and offered the right amount of guidance at all times. Also I would like to thank Dr C. Berberidis who directed me to pursue such an interesting dissertation topic.
en
heal.tableOfContents
ABSTRACT ................................................................................................................................... III
INTRODUCTION ....................................................................................................................... 1
LITERATURE REVIEW ............................................................................................................. 3
SENTIMENT ANALYSIS ................................................................................................................. 4
ASPECT-BASED SENTIMENT ANALYSIS ........................................................................................ 7
SENTIMENT ANALYSIS ON TWITTER ........................................................................................... 13
AUTOMATIC ONTOLOGY GENERATION ....................................................................................... 23
ONTOLOGY EVOLUTION ............................................................................................................. 27
PROBLEM STATEMENT ........................................................................................................ 28
CONTRIBUTION ..................................................................................................................... 29
METHODOLOGY ......................................................................................................................... 29
CREATING THE DOMAIN ONTOLOGY .................................................................................................... 29
SENTIMENT ANALYSIS ON TWEETS ...................................................................................................... 33
QUANTITATIVE ANALYSIS OUTPUTTED SENTIMENT SCORES .............................................................. 35
DIFFICULTIES ........................................................................................................................................ 50
CONCLUSIONS ...................................................................................................................... 52
BIBLIOGRAPHY ..................................................................................................................... 53
en
heal.advisorName
Kontopoulos, Dr. E.
en
heal.committeeMemberName
Vlahavas, I.
en
heal.committeeMemberName
Ass. Prof. Bassileiades, N.
en
heal.committeeMemberName
Kontopoulos, Dr. E.
en
heal.academicPublisher
School of Science &Technology, Master of Science (MSc) in Information and Communication Systems
en
heal.academicPublisherID
ihu
heal.numberOfPages
58
heal.fullTextAvailability
true
This item appears in the following Collection(s)
Related Items
Browse
-
All of IHU Repository
-
This Collection
My Account
Statistics
©International Hellenic University.
