基于机器学习法的意见挖掘系统把消费者的意见分类为积极和消极两种。

Key-sentence

Principal components analysis (PCA) is the widely used statistical method to reduce the dimension of feature set.
进行分析讨论
列举引用的文献资料
Data source
介绍数据来源和数据的处理过程
Results and discussion
Conclusion
总结model I和model II的利弊，并同 PCA、SVM等方法进行比较
References
Key-word
意见挖掘 分类 混合
opinion mining
Article introduction
讨论预测系统模型所使用的方法，
介绍model I和model II 产生的背
分为 Baseline methods 和 Hybrid
景
介绍主成分分析法的工作步骤
methods
Introduction
简要介绍文章主体内容
Background
Problem outline
列举构建模型所使用的各种方法论
Principal component analysis
Evaluating the accuracy of the model
运用Misclassification rate等指标评估 模型的准确度
Methods
Article introduction
对运用model I和model II产生的数据
Measuring the quality of hybrid opinion mining model for e-commerce application
信管X班 XXX
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Introduction Keywords
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Trend analysis
Thank you
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E-commerce has attracted more and more people to buy and sell products online, customer reviews that describe experiences with product and services are becoming more important in decision making.
Abstract

With the rapid expansion of e-commerce over the decades, the growth of the user generated content in the form of reviews is enormous on the Web. A need to organize the e-commerce reviews arises to help users and organizations in making an informed decision about the products. Opinion mining systems based on machine learning approaches are used online to categorize the customer opinion into positive or negative reviews. Different from previous approaches that employed single rule based or statistical techniques, we propose a hybrid machine learning approach built under the framework of combination (ensemble) of classifiers with principal component analysis (PCA) as a feature reduction technique. This paper introduces two hybrid models, i.e. PCA with bagging and PCA with Bayesian boosting models for feature based opinion classification of product reviews. The results are compared with two individual classifier models based on statistical learning i.e. logistic regression (LR) and support vector machine (SVM). We found that hybrid methods do better in terms of four quality measures like misclassification rate
属性、特征
classification
算法
hybrid
主要的

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algorithm 4
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principal 6
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methodology
方法论
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parameter
参数
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baseline
基线
vector
矢量
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regression
回归、衰退
procedure
程序
Key-sentence
电子商务已经吸引越来越多的人在线购买商品，描述商品和服务的评论在消费者做出 购买决定时变得越来越重要。


Opinion mining systems based on machine learning approaches are used online to categorize the customer opinion into positive or negative reviews.
在分类评论的预测模型发展进程中，为了减少错误分类，更多可靠的方法被期待应用。

References
以“意见挖掘模型”作为关键字在中国知网共搜索到文献149篇。文献分类及年度分布如下：
以英文关键字“opinion mining model”共搜索到文献9199篇。文献分类及年度分布如下：
以下列举与课题联系最为密切的几篇期刊： ［1］《基于商品特征的商品评论信息挖掘方法》【关键词】 商品评论； 观点挖掘； 情感计算； 分水岭算法；【作者】 周 民； 李蕊；2014年 ［2］《中国电子商务平台产品评论意见挖掘——基于条件随机场模型的实证研究》【关键词】 意见挖掘； 条件随机场； 电子商务平台； 用户评论； 是否评价句特征；【作者】 马晓君； 金爽； 杨淑田；2015年 ［3］《Monte Carlo Analytic Hierarchy Process (MAHP) approach to selection of optimum mining method 》【关键词】 opinion mining model；【作者】 Ataei Mohammad； Shahsavany Hashem； Mikaeil Reza；2013年 ［4］《Generating Domain-Specific Affective Ontology from Chinese Reviews for Sentiment Analysis》【关键词】 affective ontology； sentimentanalysis； product features； Chinese reviews；【作者】 刘丽珍； 刘昊； 王函石； 宋巍 ； 赵新蕾；2015年
主成分分析法是一种被广泛使用的统计方法，以减少特征集的维数。


In the development of prediction models to classify the reviews,more reliable approaches are expected to reduce the misclassifications.