A Statistical approach to evaluate the efficiency and effectiveness of the Machine Learning algorithms analyzing Sentiments

A. P. Kumar, A. Nayak, M. K
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引用次数: 2

Abstract

In the process of analyzing sentiments for a given dataset various machine learning techniques are used. The models using these learning algorithms help in determining the sentiments across the textual documents. There is a need to evaluate the effectiveness of the models in terms of analyzing and predicting sentiments. This paper provides a statistical approach to measure the effectiveness of the models and also evaluates their effectiveness with respect to the data representations. Here an experimental research is carried out with an inductive mode to measure and evaluate the models. The models are built using Decision Tree, Naive Bayes and Support Vector Machines. Data has been represented using features of Term Frequency and Inverse Document Frequency and Bag-of-words. Statistical tools used for measuring the models are Chi-square test and Analysis of Variance.
一种评估机器学习算法分析情感的效率和有效性的统计方法
在分析给定数据集的情感的过程中,使用了各种机器学习技术。使用这些学习算法的模型有助于确定文本文档中的情感。有必要评估模型在分析和预测情绪方面的有效性。本文提供了一种统计方法来衡量模型的有效性,并根据数据表示来评估它们的有效性。本文采用归纳方法对模型进行了测量和评价。模型采用决策树、朴素贝叶斯和支持向量机构建。使用词频、逆文档频率和词袋的特征来表示数据。用于测量模型的统计工具是卡方检验和方差分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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