Words and phrases bespeak the perspectives of people about products, services, governments and events on social media. Extricating positive or negative polarities from social media text denominates task of sentiment analysis in the field of natural language processing. The exponential growth of demands for business organizations and governments, impel researchers to accomplish their research in sentiment analysis. This paper leverages four state-of-the-art machine learning classifiers viz. Naïve Bayes, J48, BFTree and OneR for optimization of sentiment analysis. The experiments are performed using three manually compiled datasets; two of them are captured from Amazon and one dataset is assembled from IMDB movie reviews. The efficacies of these four classification techniques are examined and compared. The Naïve Bayes found to be quite fast in learning whereas OneR seems more promising in generating the accuracy of 91.3% in precision, 97% in F-measure and 92.34% in correctly classified instances.

中文翻译：

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使用机器学习分类器优化情感分析

词语表达了人们对社交媒体上的产品，服务，政府和事件的看法。从社交媒体文本中消除正面或负面的极性代表了自然语言处理领域中情感分析的任务。对企业组织和政府的需求呈指数增长，这促使研究人员完成了情绪分析方面的研究。本文利用了四个最新的机器学习分类器。朴素贝叶斯，J48，BFTree和OneR用于优化情感分析。使用三个手动编译的数据集进行实验；其中两个是从Amazon捕获的，一个数据集是从IMDB电影评论中收集的。检验并比较了这四种分类技术的效率。