Sentiment classification is significant in everyday life of everyone, in political activities, activities of commodity production, commercial activities. In this research, we propose a new model for Big Data sentiment classification in the parallel network environment. Our new model uses STING Algorithm (SA) (in the data mining field) for English document-level sentiment classification with Hadoop Map (M)/Reduce (R) based on the 90,000 English sentences of the training data set in a Cloudera parallel network environment — a distributed system. In the world there is not any scientific study which is similar to this survey. Our new model can classify sentiment of millions of English documents with the shortest execution time in the parallel network environment. We test our new model on the 25,000 English documents of the testing data set and achieved on 61.2% accuracy. Our English training data set includes 45,000 positive English sentences and 45,000 negative English sentences.
English Sentiment Classification using Only the Sentiment Lexicons with a JOHNSON Coefficient in a Parallel Network Environment