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Sentiment classification is the most rising research areas of sentiment analysis and text mining, especially with the massive amount of opinions available on social media. Recent results and efforts have demonstrated that there is no single strategy can mutually accomplish the best prediction performance on various datasets. There is a lack of existing researches to Arabic sentiment analysis compared to English sentiment analysis, because of the unique nature and difficulty of the Arabic language which leads to shortage in Arabic dataset used in sentiment analysis. An Arabic benchmark dataset is proposed in this paper for sentiment analysis showing the gathering methodology of the most recent tweets in different Arabic dialects. This dataset includes more than 151,000 different opinions in variant Arabic dialects which labeled into two balanced classes, namely, positive and negative. Different machine learning algorithms are applied on this dataset including the ridge regression which gives the highest accuracy of 99.90%.

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Abstract-The analysis of human activities is one of the most interesting and important open issues for the automated video surveillance community. In order to understand the behaviors of humans, a higher level of understanding is required, which is generally referred to as activity recognition. While traditional approaches rely on 2D data like images or videos, the development of low-cost depth sensors created new opportunities to advance the field. In this paper, a system to recognize human activities using 3D skeleton joints recovered from 3D depth data of RGB-D cameras is proposed. A low dimensional descriptor is constructed for activity recognition based on skeleton joints. The proposed system focuses on recognizing human activities not human actions. Human activities take place over different time scales and consist of a sequence of sub-activities (referred to as actions). The proposed system recognizes learned activities via trained Hidden Markov Models (HMMs). Experimental results on two human activity recognition benchmarks show that the proposed recognition system outperforms various state-of-the-art skeleton-based human activity recognition techniques.

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