植基於類神經網路之方法以處理不平衡語意分類

A Neural Network based Approach for Imbalanced Sentiment Classification

蔡昇哲(Sheng-Jhe Cai)

指導教授 ： 陳隆昇

朝陽科技大學/資訊學院/資訊管理系/碩士(2015年)

摘要

近年來，社群網路的蓬勃發展，使得人們除了可以面對面溝通外，還可以利用社群媒體作為電子口碑(E-WOM)傳遞之平台，多元地接收商品訊息。消費者也利用此一管道，對商品或是服務進行了解。其中透過了解負面的電子口碑，可以得知消費者的需求，並提供公司進行產品和服務上的改善以及設計個人化產品的方向。而相關研究亦證實負面的網路評論在於影響電子口碑的重要因素之一，因此如何偵測出負面評論就成為一項重要的議題。然而，當負(正)面評論多於負(正)面的評論時，就會造成分類器對多數類別範例有較高分類正確率，卻對少數類別資料有較低的偵測率，而少數類別往往是較為重要的，這就是所謂的”類別不平衡問題”(class imbalance problems)。資料探勘領域的學者逐漸重視這個問題，並提出各種處理類別不平衡的方法，如重新抽樣、特徵選取等等，使資料類別分佈可以重新取得平衡，以改善分類器對少數類別的分類效能。雖然重新抽樣的方式可以快速處理類別不平衡樣本的問題，但卻缺乏有系統的依據進行分類等等，使改善分類器(如：支持向量機、決策樹)擁有較好的分類效能，因此本研究提出「植基於倒傳遞類神經網路方法」，以倒傳遞類神經網路為基礎作為資料重新抽樣的依據，並與其他傳統處理不平衡分類的方法做比較，以證實所提方法之有效性。

關鍵字

類神經網路 ； 支持向量機 ； 不平衡語意 ； 網路口碑

並列摘要

In recent years, the rapid growth of social networks makes people not only can communicate face to face, but also can use social media as platforms for delivering electronic word of mouth (e-WOM), and receiving various product information. Customers also use these channels to get better understandings of services or products. Through obtaining negative e-WOMs, we can understand the voice of customers and thus provide them to companies for improving services and products. Negative e-WOMs also can assist us to design customized products and predict the directions of improvements. Related works also confirmed that negative e-WOM is one of important factors of influencing e-WOM distribution. Consequently, how to detect negative e-WOMs has become one of crucial issues. However, when the amount of positive e-WOMs is larger than the amount of negative e-WOMs, the classifiers induced from the collected comments will have high accuracy for the majority examples, but unacceptable error for the minority examples which are usually the important class. This is so-called “class imbalance problems”. Recently, lots of related scholars have paid attentions to this issue and presented many solutions including re-sampling, feature selection, and so on to re-balance the imbalanced class distribution to improve the classification performance. Since re-sampling methods can quickly balance the imbalance situation, but it lacks a systematic treatment of data to improve the performance of classifiers such as Support Vector Machines (SVM) and Decision Trees (DT). This study proposed the “Back-propagation Neural Network based Approach” to be the basis for re-sampling. In order to testify the effectiveness of the proposed method, the results will be compared with other traditional methods.

並列關鍵字

Support Vector Machines ； Imbalanced Semantic Classification ； online word-of-mouth ； Artificial Neural Network

參考文獻

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