LINKING ARABIC SOCIAL MEDIA BASED ON SIMILARITY AND SENTIMENT

UoM administered thesis: Phd

  • Authors:
  • Samah Alhazmi

Abstract

A large proportion of World Wide Web (WWW) users treat it as a social medium, i.e. many of them use the WWW to express and communicate their opinions. Economic value or utility can be created if these utterances, reactions, or feedback are extracted from various social media platforms and their content analysed. Some of these benefits are related to e-commerce, marketing, product improvements, improving machine learning algorithms etc. Moreover, establishing links between different social media platforms, based on shared topics and content, could provide access to the comments of users of different platforms. However, studies to date have generally tackled the area of content extraction from each type of social media in isolation. There is a lack of research of some aspects of social media, namely, linking the references from a blog post, for example, to information related to the same issue on Twitter. In addition, while studies have been carried out on various languages, there has been little investigation into social media in the Arabic language. This thesis tackles opinion mining and sentiment analysis of Arabic language social media, particularly in blogs and Twitter. The thesis focuses on Arabic language technology blogs in order to identify the expressed sentiments and then to link an issue within a blog post to relevant tweets in Twitter. This was done by assessing the similarity of content and measuring the sentiments scores. In order to extract the required data, text-mining techniques were used to build up corpora of the raw blog data in Modern Standard Arabic (MSA) and to build tools and lexicons required for this research. The results obtained through this research contribute to the field of computer science by furthering the employment of text-mining techniques, thus improving the process of information retrieval and knowledge accumulation. Moreover, the study developed new approaches to working with Arabic opinion mining and the domain of sentiment analysis.

Details

Original languageEnglish
Awarding Institution
Supervisors/Advisors
Award date31 Dec 2016