Arabic Language Sentiment Analysis using Bidirectional Long Short Term Memory

The amount of data generated in the digital era is huge since the super growth of social networks. Sentiment analysis (SA) seeks to extract opinions from a text and determine the polarity (positive, negative, or neutral). SA is widely used to refer to English. The topic of this study is SA in Arabic language. There is an amalgamation between Word2Vec and Bidirectional Long-Short Time Memory) BLSTM) used in this paper. Firstly, words in reviews are transferred into its corresponding vectors with word representation models. Secondly, sequence of words in the sentences passes as input to BLSTM. BLSTM not only captures long-range information and solves the gradient attenuation problem, but also it better represents the future semantics of the word sequence. The polarity was calculated using Word2Vec representation models, which relies on meaning and context. A BLSTM-based deep learning (DL) architecture is proposed. The result shows that BLSTM Model Architecture surpasses CNN and LSTM Architectures with the maximum accuracy of 94.88