English Sentiment Classification using Only the Sentiment Lexicons with a JOHNSON Coefficient in a Parallel Network Environment

Abstract

Sentiment classification is significant in everyday life, such as in political activities, commodity production and commercial activities. In this survey, we have proposed a new model for Big Data sentiment classification. We use many sentiment lexicons of our basis English Sentiment Dictionary (bESD) to classify 5,000,000 documents including 2,500,000 positive and 2,500,000 negative of our testing data set in English. We do not use any training data set in English. We do not use any one-dimensional vector in both a sequential environment and a distributed network system. We also do not use any multi-dimensional vector in both a sequential system and a parallel network environment. We use a JOHNSON Coefficient (JC) through a Google search engine with AND operator and OR operator to identify many sentiment values of the sentiment lexicons of the bESD in English. One term (a word or a phrase in English) is clustered into either the positive polarity or the negative polarity if this term is very close to either the positive or the negative by using many similarity measures of the JC. It means that this term is very similar to either the positive or the negative. We tested the proposed model in both a sequential environment and a distributed network system. We achieved 87.56% accuracy of the testing data set. The execution time of the model in the parallel network environment is faster than the execution time of the model in the sequential system. Our new model can classify sentiment of millions of English documents based on the sentiment lexicons of the bESD in a parallel network environment. The proposed model is not depending on both any special domain and any training stage. This survey used many similarity coefficients of a data mining field. The results of this work can be widely used in applications and research of the English sentiment classification.