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Sentiment Analysis on Movie Reviews: A Comparative Study of Machine Learning Algorithms and Open Source Technologies

机译:电影评论的情感分析:机器学习算法与开源技术的比较研究

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Social Networks such as Facebook, Twitter, Linked In etc… are rich in opinion data and thus Sentiment Analysis has gained a great attention due to the abundance of this ever growing opinion data. In this research paper our target set is movie reviews. There are diverge range of mechanisms to express their data which may be either subjective, objective or a mixture of both. Besides the data collected from World Wide Web consists of lot of noisy data. It is very much true that we are going to apply some pre-processing techniques and compare the accuracy using Machine Learning algorithm Na?ve Bayes Classifier. With ever growing demand to mine the Big Data the open source software technologies such as Hadoop using map reducing paradigm has gained a lot of pragmatic importance. This paper illustrates a comparitive study of sentiment analysis of movie reviews using Na?ve Bayes Classifier and Apache Hadoop in order to calculate the performance of the algorithms and show that Map Reduce paradigm of Apache Hadoop performed better than Na?ve Bayes Classifier.
机译:诸如Facebook,Twitter,Linked In等之类的社交网络拥有丰富的意见数据,因此,由于这种不断增长的意见数据的丰富性,情绪分析得到了极大的关注。在这篇研究论文中,我们的目标是电影评论。有多种机制来表达其数据,这些机制可以是主观的,客观的或两者的混合。此外,从万维网收集的数据还包含许多嘈杂的数据。确实,我们将应用一些预处理技术,并使用机器学习算法Naveve Bayes分类器比较准确性。随着挖掘大数据的需求不断增长,使用地图缩减范例的诸如Hadoop之类的开源软件技术已具有许多务实的重要性。本文说明了使用朴素贝叶斯分类器和Apache Hadoop对电影评论进行情感分析的比较研究,以便计算算法的性能,并表明Apache Hadoop的Map Reduce范式比朴素贝叶斯分类器表现更好。

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