Improving the Polarity of Text through word2vec Embedding for Primary Classical Arabic Sentiment Analysis.

Over the past decade, Sentiment analysis has attracted significant researcher attention. Despite a huge number of studies in this field, Sentiment analysis of authors' books (classical Arabic) with extracting the embedding features has not yet been done. The recent feature extraction of Arabic text depends on the frequency of the words within the corpus without extracting the relation between these words. This paper aims to create a new classical Arabic dataset CASAD from many art books by collecting sentences from several stories with human-expert labeling. Additionally, the feature extraction of those datasets is created by word embedding techniques equivalent to Word2vec that are able to extract the deep relation which means features of the formal Arabic language. These features are evaluated by several types of machine learning for classical Arabic, for example, support vector machines (SVM), Logistic Regression (LR), Naive Bayes (NB) K-Nearest Neighbors (KNN), Latent Dirichlet Allocation (LDA) and Classification And Regression Trees (CART). Moreover, statistical methods such as validation and reliability are applied to evaluate this dataset's label. Finally, our experiments evaluated the classification rate of the feature-extraction matrices in two and three classes using six machine-learning algorithms for tenfold cross-validation that showed that the Logistic Regression with Word2Vec approach is the most accurate in predicting topic-polarity occurrence.