Biblio

Nowadays, sentiment analysis methods become more and more popular especially with the proliferation of social media platform users number. In the same context, this paper presents a sentiment analysis approach which can faithfully translate the sentimental orientation of Arabic Twitter posts, based on a novel data representation and machine learning techniques. The proposed approach applied a wide range of features: lexical, surface-form, syntactic, etc. We also made use of lexicon features inferred from two Arabic sentiment words lexicons. To build our supervised sentiment analysis system, we use several standard classification methods (Support Vector Machines, K-Nearest Neighbour, Naïve Bayes, Decision Trees, Random Forest) known by their effectiveness over such classification issues. In our study, Support Vector Machines classifier outperforms other supervised algorithms in Arabic Twitter sentiment analysis. Via an ablation experiments, we show the positive impact of lexicon based features on providing higher prediction performance.

Segmentation of land and water regions is necessary in many applications involving analysis of remote sensing imagery. Not only is manual segmentation of these regions prone to considerable subjective variability, but the large volume of imagery collected by modern platforms makes manual segmentation extremely tedious to perform, particularly in applications that require frequent re-measurement. This paper examines a robust, semi-automated approach that utilizes simple and efficient machine learning algorithms to perform supervised classification of multi-spectral image data into land and water regions. By combining the four wavelength bands widely available in imaging platforms such as IKONOS, QuickBird, and GeoEye-1 with basic texture metrics, high quality segmentation can be achieved. An efficient workflow was created by constructing a Graphical User Interface (GUI) to these machine learning algorithms.