Sentiment Analysis of Tweets Using Various Machine Learning Techniques

Ankit Tariyal, Sachin Goyal, Neeraj Tantububay
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引用次数: 10

Abstract

In todays e-commerce market where online shopping and tourism is fastly growing so it very important to analyze such huge amount of large data present in web. So it is very important to create a method which classify the web data. Sentiment analysis is a method to classify the web data such as product reviews, views in to various polarities such a positive, negative or neutral. In this paper we classify the reviews by using various machine learning techniques, In this we create a various classification model and compute the performance of each models and select the best classification models based on their performance computation. We will use a combination of simple linear method (LDA), nonlinear methods (CART, KNN) and complex nonlinear methods (SVM, RF, C5.0).
使用各种机器学习技术对推文进行情感分析
在网上购物和网上旅游迅速发展的今天,对网络中存在的大量大数据进行分析是非常重要的。因此,建立一种对网络数据进行分类的方法显得尤为重要。情感分析是一种将网络数据(如产品评论、观点)分类为积极、消极或中性等不同极性的方法。在本文中,我们使用各种机器学习技术对评论进行分类,在此我们创建了各种分类模型并计算每个模型的性能,并根据它们的性能计算选择最佳分类模型。我们将使用简单线性方法(LDA),非线性方法(CART, KNN)和复杂非线性方法(SVM, RF, C5.0)的组合。
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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