Since the explosion of opinion-based web content, sentiment analysis has drawn the researchers ' interest in recent years as an extension of natural language processing. It area has brought a lot of growth which has encouraged the achievement of optimum classification of text results. We provide a collection of Advanced pattern-based applications to identify tweets along with other tools. In this article, together with their hybrid variations, we worked with the commonly used standard classifiers and machine learning to refine specific parameters so as to obtain the best possible classification accuracy. We performed our studies and provided relevant results and observations on the named film review corpus. The importance of opinion prediction via Facebook is investigated in this paper using machine learning approaches. Twitter-specific social network structures, like retweets, are also taken into account. Additionally, we are concentrating on locating both short and long phrases that are pertinent to the situation so that we can comprehend its effects. We used supervised machine learning methods such supporting vector machines (SVM), Naive Bayes, maximum entropy, and artificial neural networks to categorise data from Twitter using unigram, bigram, and unigram. The Bigram (hybrid) Extraction Model's Function.