AN EFFECTIVE HYBRID STOCHASTIC GRADIENT DESCENT ARABIC SENTIMENT ANALYSIS WITH PARTIAL-ORDER MICROWORDS AND PIECEWISE DIFFERENTIATION

Social media networking sites, such as Instagram, Facebook, and Twitter, have become an inextricable part of our everyday lives. These social media networks are useful for sharing news, images, and other information. The study of social media sentiment has recently received a lot of attention, especially in Arabic sentiment analysis. Social media sites are distinguished by unusual language that differs from the traditional format of the language. As a result, there is a necessity for efficient ways for analyzing the massive amount of new word variants that appear regularly in the digital world and online world. This study proposes a piecewise Stochastic Gradient Descent (SGD)-based model for sentiment classification. The TF-IDF-based term weighting scheme is employed for textual feature representation. For enhancing the model performance, stemming and partially ordered microword representation of tweets with varying look ahead distances is employed. Also, various n-gram models are considered for textual feature representation, which also improves the model performance. The proposed model is simulated and evaluated with the help of publicly available tweet corpus 9 which is a balanced tweet corpus. The effectiveness of the proposed model is estimated using various performance evaluation metrics. According to the experimental observations, the proposed method accurately categorizes the testing and validation tweets set with an accuracy of up to 92.23%.