情报杂志
情報雜誌
정보잡지
JOURNAL OF INFORMATION
2013年
2期
117-120
,共4页
商品主观评论%文本挖掘%情感细分类%情感倾向分析%支持向量机(SVM)% 人工神经网络(ANN)
商品主觀評論%文本挖掘%情感細分類%情感傾嚮分析%支持嚮量機(SVM)% 人工神經網絡(ANN)
상품주관평론%문본알굴%정감세분류%정감경향분석%지지향량궤(SVM)% 인공신경망락(ANN)
product reviews% text mining% concrete sentiment classification %sentiment analysis %Support Vector Machine (SVM)% Ar-tificial Neural Network (ANN)
在现有褒贬性情感分类的研究中,缺乏对商品具体属性情感倾向的分析.基于此,建立细分类模型,将情感分类分为初分类和细分类两个过程.初分类确定商品评论的整体情感倾向,根据初分类的结果对商品的各个属性再次进行情感分类,以确定具体属性的情感倾向.从而消费者无需阅读具体的文本评论,就可以全面直观地了解商品,缩短做出购买决策的时间,降低决策的复杂度.该模型可作为网上商品销售的一个扩展功能使用,并利用酒店评论文本检测了模型的有效性.同时,论文通过对四种经典的特征算法的测试,发现在情感细分类中互信息(Mutual Information,MI)达到了更高的准确度.
在現有褒貶性情感分類的研究中,缺乏對商品具體屬性情感傾嚮的分析.基于此,建立細分類模型,將情感分類分為初分類和細分類兩箇過程.初分類確定商品評論的整體情感傾嚮,根據初分類的結果對商品的各箇屬性再次進行情感分類,以確定具體屬性的情感傾嚮.從而消費者無需閱讀具體的文本評論,就可以全麵直觀地瞭解商品,縮短做齣購買決策的時間,降低決策的複雜度.該模型可作為網上商品銷售的一箇擴展功能使用,併利用酒店評論文本檢測瞭模型的有效性.同時,論文通過對四種經典的特徵算法的測試,髮現在情感細分類中互信息(Mutual Information,MI)達到瞭更高的準確度.
재현유포폄성정감분류적연구중,결핍대상품구체속성정감경향적분석.기우차,건립세분류모형,장정감분류분위초분류화세분류량개과정.초분류학정상품평론적정체정감경향,근거초분류적결과대상품적각개속성재차진행정감분류,이학정구체속성적정감경향.종이소비자무수열독구체적문본평론,취가이전면직관지료해상품,축단주출구매결책적시간,강저결책적복잡도.해모형가작위망상상품소수적일개확전공능사용,병이용주점평논문본검측료모형적유효성.동시,논문통과대사충경전적특정산법적측시,발현재정감세분류중호신식(Mutual Information,MI)체도료경고적준학도.
In present research of appraisable classification, there are almost no records of the sentiment inclination of the products' attrib-utes. This paper constructs the concrete sentiment classification model by dividing the project into two processes:preliminary classification and concrete classification. The former will determine the overall sentiment orientation of reviews, the result of the process can be used for the further classification of each attribute of the product to determine the sentiment orientation of the product's each attribute. Thus, without reading comments, consumers can get a comprehensive and intuitive understanding of the product vividly. It helps shorten the time needed in making a buying decision and reduce the complexity involved. In this paper, the online hotel comments are used to validate the model. The model can augment the function of the transaction virtual community. Meanwhile, in the validation test, the effect of the Mutual In-formation (MI) is found to be more accurate.
