Sentiment Analysis Techniques –Survey

Abstract

Online social media platforms allow people to express their ideas and opinions about anything as products, education, or politics, this produce a huge data requires appropriate tools to analyze it accurately, and that’s why Sentiment Analysis (SA) has become more used in recent years. This paper review the SA and classification methods of machine learning especially supervised techniques like Support Vector Machine (SVM), Neural Networks (NN), Naïve Bayes (NB), Rough Set Theory (RST), Random Forest (RF), Decision Trees (DT), and other technology of SA is lexicon techniques and hybrid methods (which include the lexicon and machine learning techniques). Machine learning consist of two group supervised (Classification and Regression) and unsupervised (clustering and association). The SA Affected from one system to another based on its polarity as “positive” or “negative”, and the language of data that processed.