A Survey on Sentiment Analysis Algorithms and Techniques For Arabic Textual Data

Abstract

The concept Sentiment means the feeling, behavior, belief, or attitude towards something that almost being embedded. sentiment analysis is the process of analyzing, extracting, studying, and classifying the various reviews, opinions are given by people, and human’s emictions into positive, negative, neutral. It is considered one of the most significant scientific branches that aim to determine the behavior of the speaker, the attitude of the writer according to some topic, or the overall emotional reaction to website, document, event, interaction, products, or services. many users can share every day various opinions on different topics that may be detected or embedded by using micro-blogging which considered a rich resource for sentiment analysis and belief mining such as Facebook, Twitter, forums, and Blogs. recently a huge number of posted comments, tweets, and reviews of different social media websites include rich information in addition to most of the on-line shopping sites provide the opportunity to customers to write reviews about products in order to enhance the sales of those products and to improve both of product quality and customer satisfaction. manual analysis of these large reviews is practically impossible thus it is needed to discover an automated approach to solving such a hard process. In the Middle East and particularly in the Arab world, social media websites continue to be the top-visited websites especially with the current social and political changes in this part of the world. the main objective of that research is to differentiate between various algorithms and techniques of sentiment analysis and classification dependent on the Arabic language as a little number of researchers discusses that point relevant to the Arabic language. Different algorithms and techniques of data mining such as Support Vector Machine (SVM), Naïve Bayes (NB), Bayesian Network (BN), Decision tree (DT), k-nearest neighbor (KNN), Maximum Entropy (ME), and Neural Network (NN) in addition to many other alternative techniques which are used for analyzing and classifying textual data. For the reasons of difficulties in analyzing and mining a large number of linguistic words for their Those techniques are estimated based on the Arabic language due to its richness and diversity. The comparison between data mining techniques showed that the most accurate technique is the support vector machine (SVM) algorithm. every successful sentiment depends on two essential analysis tools are language and culture.