Rawan T. Khasawneh

Sentiment analysis of arabic social media content: a comparative study

The Internet became an indispensable part of peoples lives because of the significant role it plays in the ways individuals interact, communicate and collaborate with each other. Over recent years, social media sites succeed in attracting a large portion of online users where they become not only content readers but also content generators and publishers. Social media users generate daily a huge volume of comments and reviews related to different aspects of life including: political, scientific and social subjects. In general, sentiment analysis refers to the task of identifying positive and negative opinions, emotions and evaluations related to an article, news, products, services, etc. Arabic sentiment analysis is conducted in this study using a small dataset consisting of 1,000 Arabic reviews and comments collected from Facebook and Twitter social network websites. The collected dataset is used in order to conduct a comparison between two free online sentiment analysis tools: SocialMention and SentiStrength that support Arabic language. The results which based on based on the two of classifiers (Decision tree (J48) and SVM) showed that the SentiStrength is better than SocialMention tool.

Arabic sentiment polarity identification using a hybrid approach

Recent years witness a significant increase in research related to knowledge extraction from web social networks or media. The enormous volume of posted comments, and related media can be a rich source of information. In Middle East and the Arab world in particular, social media websites continue to be the top visited websites especially with the current social and political changes in this part of the world. Sentiment analysis and opinion mining focus on identifying and evaluating positive and negative opinions and comments. This study aims to identify the sentiment polarity for collected comments or posts from Twitter using a hybrid approach and a modest dataset of Arabic (Text and audio) comments. Two machine learning classification techniques are used to perform the required classification to identify the polarity of the collected opinions. We extended the evaluation of prediction algorithms and enhance them using Bagging and Boosting algorithms. We extracted a unified dataset of texts, audios and images and applied processing methods to extract final sentiment opinions. We noticed that some special expressions specially in recoding (such as laughing, yelling, etc. within the recording) have a negative effect on the accuracy of the automatic sentiment prediction system.

Analysis of JAK2V617F mutation in Jordanian patients with myeloproliferative neoplasms

OBJECTIVE/BACKGROUND Myeloproliferative neoplasms (MPNs) are heterogeneous clonal bone marrow stem cell disorders and include polycythemia vera (PV), essential thrombocythemia (ET), and idiopathic myelofibrosis (IMF) neoplasia. In 2005, the JAK2(V617F) mutation was identified in Philadelphia chromosome-negative patients. The aim of this study was to sequence coding exons 12 and 14 of the JAK2 gene in Jordanian patients with MPN. METHODS Both exons 12 and 14 of the JAK2 gene were amplified using polymerase chain reaction from DNA extracted from 68 blood and bone marrow samples belonging to 57 MPN patients and subjected to DNA sequencing. RESULTS JAK2(V617F) mutations were detected in 26 of 57 Jordanian patients (45%) with different MPNs. JAK2(V617F) was identified in 70%, 31%, and 14% of PV, ET, and IMF cases, respectively. Five men diagnosed with PV were homozygous for JAK2(V617F), whereas the other 21 patients were heterozygous for the mutation. Neither the JAK2(V617F) mutation nor any DNA polymorphism in exon 12 or exon 14 of the JAK2 gene was detected among the 40 leukemic patients. A rare single nucleotide polymorphism, c.1860C→T (rs375442615), was detected in one patient with ET. CONCLUSION This study is the first molecular investigation of the JAK2 gene in Jordan. We successfully identified the JAK2(V617F) mutation in Jordanian patients with Philadelphia chromosome-negative MPNs. Our results provide a basis for the early detection of this mutation and simplify the diagnostic workup for these disorders at the molecular level.