Sentiment classification plays a dominant role in day-to-day life including the political events, production areas, and commercial activities. The need for the accurate and instant classification of the user emotions is a hectic task to be solved. The traditional methods fail to address the classification of dynamic dataand huge volumes of documents. Moreover, to assure the classification accuracy and deal with huge volumes of data, the proposed method employs the MapReduce Framework. The proposed sentiment analysis involves twoprocesses, such as feature extraction and classification that is performed in the MapReduce framework using the mapper and reducer functions. The feature vector is based on the sentence-specific features, SentiWordNetbasedfeatures, and statistical features corresponding to the individual reviews that are classified as positive and negative reviews using the proposed Chronological-Brain Storm Optimization based Support Vector Neural Network (CBSO-SVNN). The analysis is progressed using four datasets obtained from the movie reviewdatabase that confirms that the proposed method outperformed the existing methods in terms of the accuracy, sensitivity, and specificity. The accuracy, specificity, and sensitivity of the proposed method are 0.8714, 0.9027,and 0.8714, respectively.