An improved K-nearest-neighbor algorithm using genetic algorithm for sentiment classification

机译：基于遗传算法的情感分类的改进的K近邻算法

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Sentiment classification is to find the polarity of product or user reviews. Supervised machine learning algorithms are used for opinion mining such as Navie Bayes, K-nearest neighbor and Support vector machine. KNN is simple algorithm but less efficient classification algorithm. In this paper we propose an improved KNN algorithm, genetic algorithm is developed which is a hybrid genetic algorithm that incorporates the information gain for feature selection and combined with KNN to improve its classification performance. Specifically, we compared other supervised machine learning approaches such as Navie Bayes and traditional kNN for Sentiment Classification of movie reviews and book reviews. The experimental results using genetic algorithm with improved indicate high performance levels with Fmeasure of over 87% on the movie reviews.

机译：情感分类是为了找到产品或用户评论的极性。监督式机器学习算法用于意见挖掘，例如Navie Bayes，K近邻和支持向量机。 KNN是简单算法，但效率较低的分类算法。在本文中，我们提出了一种改进的KNN算法，即遗传算法，它是一种混合遗传算法，它融合了信息增益以进行特征选择，并与KNN相结合以提高其分类性能。具体来说，我们比较了其他受监督的机器学习方法（例如Navie Bayes和传统的kNN）对电影评论和书评的情感分类。使用改进的遗传算法进行的实验结果表明，影片评论中的Fmeasure达到了87％以上的高性能水平。

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《International Conference on Circuit, Power and Computing Technologies》|2014年|1647-1651|共5页
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Kalaivani P.; Shunmuganathan K.L.;
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Opinion mining; features selection; genetic algorithm; machine learning algorithm; sentiment classification;

机译：观点挖掘;特征选择;遗传算法;机器学习算法;情感分类;

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机译：分类算法基本原则K-最近邻，支持向量机，随机森林和神经网络：综述

An improved K-nearest-neighbor algorithm using genetic algorithm for sentiment classification

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