Twitter social media data generally uses ambiguous text that can cause difficulty in identifying positive or negative sentiments. There are more than one billion social media messages that need to be stored in a proper database and processed correctly to analyze them. In this paper, an ensemble majority vote classifier to enhance sentiment classification performance and accuracy is proposed. The proposed classification model is combined with four classifiers, using varying techniques—naive Bayes, decision trees, multilayer perceptron and logistic regression—to form a single ensemble classifier. In addition to these, a comparison is drawn among the four classifiers to evaluate the performance of the individual classifiers. The result shows that in terms of an individual classifier, the naive Bayes classifier is optimal as compared to the others. However, for comparing the proposed ensemble majority vote classifier with the four individual classifiers, the result illustrates that the performance of the proposed classifier is better than the independent one.

Today, images are a part of communication between people. However, images are being used to share information by hiding and embedding messages within it, and images that are received through social media or emails can contain harmful content that users are not able to see and therefore not aware of. This paper presents a model for detecting spam on images. The model is a combination of optical character recognition, natural language processing, and the machine learning algorithm. Optical character recognition extracts the text from images, and natural language processing uses linguistics capabilities to detect and classify the language, to distinguish between normal text and slang language. The features for selected images are then extracted using the bag-of-words model, and the machine learning algorithm is run to detect any kind of spam that may be on it. Finally, the model can predict whether or not the image contains any harmful content. The results show that the proposed method using a combination of the machine learning algorithm, optical character recognition, and natural language processing provides high detection accuracy compared to using machine learning alone.

The aim of this paper is the design and development a new English-Arabic neural machine translation (NMT) called DIA translation system. The main purpose of the designing system is to study translator limited sulfur industry domain as a stand-alone tool in order to improve the translation quality. Machine translation (MT) are very sensitive to the domains they were trained on and can be integrated with general (English-Arabic) MT systems. The proposed system has mainly four directions: supports chemical symbols, terms, phrase, and text and it is evaluated by using (1,200) various English declarative sentences which written by English Language experts. The obtained results indicate that this system is high effective and has an accuracy of 79.33% in comparison with Google translator which has 38.67% for the same test samples.