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Natural Language Processing (NLP) is a computer program utilized in big data applications to specify the customer review also it's a major part of AI. Thus NLP strategy is processed with several languages because the language is differing from state to state. Furthermore, sentiment analysis in NLP is advanced in many applications and various languages to evaluate the sentiment value. But part of speech specification for different language is too difficult. To overcome this problem the current research aimed to develop a novel Evolving C4.5 machine learning with Spider Monkey Optimization (EC4.5-ML-SMO) to classify the sentiment analysis in Telugu language efficiently. Moreover, the fitness function of SMO enhanced the accuracy of sentiment classification in Telugu dataset. Finally, the effectiveness of proposed module is evaluated with recent existing works and attained better result by achieving better classification accuracy rate.

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