No abstract

In the last few years, and due to the vast widespread of social media applications, texts have become more important and get more attention. Since texts, in general, are carrying a lot of information that can be extracted and analyzed. Several researchers have done significant works in text classification. Within different scripts such as English and other western languages, several challenges and obstacles have been addressed with such a field of research. Regarding the Arabic language, the process is different from other vital languages since Arabic is considered an orthographic language that depends on the word shape. It is not easy to apply the standard text preprocessing techniques since it affects the word meaning. This paper evaluates Arabic short text classification using three standard Naïve Bayes classifiers. In our method, we classify the thesis and dissertations using their titles to perform the classification process. The collected dataset is collected from different repositories by using standard scrapping techniques. Our method classifies the document based on their titles to be placed in the desired specialization. Several preprocessing techniques have been applied, such as (punctuation removal, stop words removal, and space vectorization). For feature extraction, we adopt the TF-IDF method. We implemented three types of Naïve Bayes classifiers which are (Multinomial Naïve Bayes, Complemented Naïve Bayes, and Gaussian Naïve Bayes). The study results showed that Complemented Naïve Bayes Classifier proposed the best performance with (0.84) of accuracy for the testing phase. The results of the study are promising to be applied with different short text classifications.

Covid-19 pandemic affected our daily activities. Education is the fastest aspect that has been hindered due to the pandemic. The majority of the schools and universities were prone to global closure. The world turned toward using e-learning as an alternative to traditional learning due to the strict lockdown procedures. This study presents a model to measure the students' intention to use e-learning during the pandemic in Iraq. Technology Acceptance Model (TAM) is utilized with some external variables. Task Technology Fit (TTF) is also adopted as a moderator for the proposed model. The model is implemented on a sample of students studying at Technical College of Management -Baghdad who have completed the academic year using e-learning. Eighteen hypotheses were formulated; out of them, only two were rejected. The model is tested and evaluated using PLS Tool. The results showed that the students have a positive intention to use e-learning during the covid-19 pandemic; also, most of the external variables were statistically significant. Furthermore, using TTF positively moderates the proposed model. The results open the way to perform more investigations in adopting e-learning during unusual situations.