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Many investigations have performed sentiment analysis to gauge public opinions in various languages, including English, French, Chinese, and others. The most spoken language in South Asia is Urdu. However, less work has been carried out on Urdu, as Roman Urdu is also used in social media (Urdu written in English alphabets); therefore, it is easy to use it in English language processing software. Lots of data in Urdu, as well as in Roman Urdu, are posted on social media sites such as Instagram, Twitter, Facebook, etc. This research focused on the collection of pure Urdu Language data and the preprocessing of the data, applying feature extraction, and innovative methods to perform sentiment analysis. After reviewing previous efforts, machine learning and deep learning algorithms were applied to the data. The obtained results were compared, and hybrid methods were also recommended in this research, enabling new avenues to conduct Urdu language data sentiment analysis. [ABSTRACT FROM AUTHOR]

Considered as one of the emerging evolutionary algorithms, the Dendritic Cell Algorithm (DCA) is based on the behavior of specific immune agents; known as Dendritic Cells (DCs). Studies related with DCA are increasingly becoming popular and this is due to the worthy characteristics expressed by the algorithm as it exhibits several potentially beneficial features for binary classification problems. Yet, according to our best knowledge, there is no study summarizing the basic features of the DCA developed versions all in one paper. Hence, in this paper we aim at summarizing the powerful characteristics of the DCA while making a general review of the algorithm. We aim at studying the various versions of DCA while highlighting their characteristics, advantages and limitations. Based on this study and from the conducted conclusions, we intend to generate a well-studied novel DC classifier based on the positive aspects reflected by the previously proposed DCA versions. Results show that our proposed algorithm succeeds in obtaining significantly improved classification accuracy. [ABSTRACT FROM AUTHOR]