發(fā)布時間：2018-12-14 08:31

【摘要】：隨著電子商務(wù)的迅猛發(fā)展,網(wǎng)絡(luò)購物已經(jīng)成為當下中國最熱的潮流。消費者在進行網(wǎng)絡(luò)購物的同時,也會根據(jù)網(wǎng)絡(luò)購物過程的體驗和產(chǎn)品使用情況發(fā)表用戶評論。每一條評論都是帶有情感色彩的,是用戶一種情緒的表達,或是喜歡、厭惡,或是對商家的意見和建議,準確及時地獲知這些用戶情緒對商家的經(jīng)營決策至關(guān)重要。然而這些用戶評論數(shù)量巨大且屬非結(jié)構(gòu)化信息,只關(guān)注每一條個體情緒是沒有意義的,也是不可操作的。因此,需要將所有用戶評論綜合起來,研究用戶群體情緒的變化,從而有效地支持商家的經(jīng)營決策。本研究主要針對大型B2C電子商務(wù)網(wǎng)站上用戶評論進行研究,使得這些評論信息可以有效指導(dǎo)商家的經(jīng)營決策,具體研究內(nèi)容如下:(1)文章首先對用戶評論數(shù)據(jù)進行預(yù)處理,采用關(guān)聯(lián)規(guī)則尋找頻繁項集的方法實現(xiàn)了產(chǎn)品特征的抽取。根據(jù)已抽取到的產(chǎn)品特征,利用產(chǎn)品特征詞和情感傾向詞的共現(xiàn)關(guān)系實現(xiàn)了“產(chǎn)品特征—情感傾向”詞對的抽取。然后,基于模糊數(shù)理論構(gòu)建了情感傾向詞模糊語料庫,計算出了情感傾向詞的模糊隸屬度函數(shù)和情感極性值。并采用模糊認知圖對“產(chǎn)品特征—情感傾向”詞對及其情感極性值進行了知識表示,構(gòu)建了“產(chǎn)品特征—情感傾向”綜合分析模型;(2)在此基礎(chǔ)上,利用已經(jīng)抽取到的“產(chǎn)品特征—情感傾向”詞對及其情感極性值構(gòu)建了源案例庫�；谧C據(jù)理論對“產(chǎn)品特征—情感傾向”詞對進行兩兩融合,實現(xiàn)了群體情緒的識別,并進行了群體情緒識別實驗;(3)最后,文章基于時間序列實現(xiàn)了群體情緒的演化研究,追蹤了群體情緒的變化過程和趨勢,這是本文最大的創(chuàng)新點。并依據(jù)群體情緒演化結(jié)果,指導(dǎo)了商家的經(jīng)營決策。本文把群體情緒的概念引入到用戶評論挖掘中來,并基于時間序列對群體情緒的演化過程進行研究,在理論方面能夠進一步豐富充實網(wǎng)絡(luò)用戶評論領(lǐng)域的研究;在實踐方面,可以使商家準確及時地掌握用戶的需求、喜好及變化趨勢,更加高效的指導(dǎo)商家經(jīng)營決策。
[Abstract]:With the rapid development of e-commerce, online shopping has become the hottest trend in China. While shopping online, consumers will also make comments according to the experience of online shopping process and the use of products. Each comment is emotional, it is the expression of a kind of user's emotion, or likes, dislikes, or to the merchant's opinion and suggestion, accurate and timely know these user's emotion is very important to the businessman's management decision. However, these users comment on a large number of unstructured information, focusing on each individual emotion is meaningless, and not operational. Therefore, it is necessary to synthesize all user reviews and study the changes of user group emotions, so as to effectively support business decisions. This research mainly focuses on the user comments on large B2C e-commerce websites, which can effectively guide the business decisions. The specific contents are as follows: (1) the paper preprocesses the user comment data. Product feature extraction is realized by using association rules to find frequent itemsets. According to the extracted product features, the cooccurrence relationship between product feature words and affective predisposition words is used to realize the extraction of "product-affective tendency" word pairs. Then, based on the fuzzy number theory, the fuzzy corpus of affective predisposition words is constructed, and the fuzzy membership function and affective polarity value of affective predisposition words are calculated. The fuzzy cognitive map is used to express the word pair of "product feature-affective propensity" and its affective polarity value, and a comprehensive analysis model of "product feature-affective propensity" is constructed. (2) on this basis, the source case database is constructed by using the extracted word pair of "product character-affective tendency" and its affective polarity value. Based on the evidence theory, this paper combines the word pairs of "product characteristics and affective tendency", realizes the recognition of group emotion, and carries on the experiment of group emotion recognition. (3) finally, based on the time series, this paper realizes the research of the evolution of group emotion, and tracks the changing process and trend of group emotion, which is the most innovative point of this paper. And according to the group emotion evolution result, has guided the merchant's management decision. In this paper, the concept of group emotion is introduced into user comment mining, and the evolution process of group emotion is studied based on time series, which can enrich the research of network user comment field in theory. In practice, the business can accurately and timely grasp the user's needs, preferences and trends, and guide business decisions more efficiently.
【學位授予單位】：東華大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：F724.6;F713.55

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