本文選題：情感分析 + 電商評(píng)論��； 參考：《東華大學(xué)》2017年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)的發(fā)展特別是智能移動(dòng)端的普及,越來(lái)越多的用戶通過(guò)網(wǎng)絡(luò)電商平臺(tái)進(jìn)行購(gòu)物并留下評(píng)論。評(píng)論內(nèi)容可以反映出用戶的喜好以及商品的優(yōu)缺點(diǎn),直接影響著潛在客戶的購(gòu)買意向,也為生產(chǎn)廠商和銷售企業(yè)提供決策依據(jù)。于是各種各樣的情感分析技術(shù)被應(yīng)用到評(píng)論領(lǐng)域。如何將這些評(píng)論內(nèi)容的情感分析結(jié)果應(yīng)用到生產(chǎn)實(shí)際中去是一個(gè)很值得研究的問(wèn)題。評(píng)論中的句子不同于論壇中或者微博中的句子,它們大多數(shù)簡(jiǎn)短并且表達(dá)主語(yǔ)不明確,因此基于語(yǔ)義規(guī)則對(duì)評(píng)論進(jìn)行研究能取得更好的效果,但是已有的語(yǔ)義規(guī)則并不能滿足評(píng)論中特有的句子結(jié)構(gòu),有待進(jìn)一步改進(jìn)。本文首先介紹了情感分析的理論、背景、研究現(xiàn)狀和相關(guān)技術(shù);然后以服裝電商評(píng)論為研究對(duì)象,提出一種基于語(yǔ)義規(guī)則的服裝電商評(píng)論情感分析流程。重點(diǎn)總結(jié)了中文評(píng)論句子結(jié)構(gòu)規(guī)則,提出了一種量化情感傾向強(qiáng)度的計(jì)算方法;進(jìn)而提出了一種銷量預(yù)測(cè)模型來(lái)研究情感傾向強(qiáng)度與銷量之間的關(guān)系。通過(guò)使用分類算法分析影響銷量的最佳評(píng)論頁(yè)數(shù);最后通過(guò)一個(gè)應(yīng)用案例對(duì)研究成果進(jìn)行驗(yàn)證。技術(shù)上主要采用Python語(yǔ)言實(shí)現(xiàn),首先爬取淘寶上評(píng)分不同的兩個(gè)商品4000多條評(píng)論數(shù)據(jù)建立原始語(yǔ)料庫(kù),并使用哈爾濱工業(yè)大學(xué)的語(yǔ)言技術(shù)平臺(tái)對(duì)原始語(yǔ)料庫(kù)進(jìn)行分詞和相關(guān)標(biāo)注生成XML語(yǔ)料庫(kù);其次借助XML語(yǔ)料庫(kù)和通用情感詞典建立服裝領(lǐng)域情感詞典并使用該詞典對(duì)XML語(yǔ)料庫(kù)進(jìn)行修正;然后采用本文提出的語(yǔ)義計(jì)算規(guī)則計(jì)算每條評(píng)論的情感傾向強(qiáng)度值;進(jìn)而使用貝葉斯等七種分類算法分析情感傾向強(qiáng)度值、評(píng)論頁(yè)數(shù)與銷量之間的關(guān)系,使用召回率和精確度評(píng)估確定最佳評(píng)論頁(yè)數(shù);最后根據(jù)最佳評(píng)論頁(yè)數(shù)分別使用線性回歸模型、神經(jīng)網(wǎng)絡(luò)模型和支持向量機(jī)回歸模型通過(guò)預(yù)測(cè)銷量來(lái)分析情感傾向強(qiáng)度與銷量之間的關(guān)系。
[Abstract]:With the development of the Internet, especially the popularity of intelligent mobile terminals, more and more users shop and leave comments through the network e-commerce platform. The content of comments can reflect the preferences of users and the advantages and disadvantages of products, directly affect the purchase intention of potential customers, and also provide decision basis for manufacturers and sales enterprises. As a result, a variety of emotional analysis techniques have been applied to the field of comment. How to apply the emotional analysis results of these comments to the production practice is a problem worth studying. The sentences in comments are different from those in forums or Weibo. Most of them are short and ambiguous, so it is more effective to study comments based on semantic rules. However, the existing semantic rules can not meet the specific sentence structure in comments, and need to be further improved. This paper first introduces the theory, background, research status and related technology of emotional analysis, and then takes the clothing e-commerce review as the research object, proposes a semantic rule based emotional analysis process of clothing ecommerce review. In this paper, the structure rules of Chinese comment sentences are summarized, a method to calculate the intensity of affective tendency is proposed, and a sales prediction model is proposed to study the relationship between the intensity of emotional tendency and the sales volume. The optimal number of comment pages affecting sales volume is analyzed by using the classification algorithm. Finally, an application case is used to verify the research results. Technically, it is mainly implemented in Python language. First of all, the original corpus is built by crawling more than 4000 comments on two items with different scores on Taobao. Using the language technology platform of Harbin University of Technology, the original corpus is segmented and annotated to generate XML corpus. Secondly, the XML corpus and the general emotion dictionary are used to establish the clothing domain emotion dictionary and the XML corpus is modified by the dictionary, and then the intensity of the emotional tendency of each comment is calculated by the semantic calculation rule proposed in this paper. Then seven classification algorithms, such as Bayes, are used to analyze the intensity of emotional tendency, the relationship between the number of comment pages and the sales volume, and the recall rate and accuracy evaluation are used to determine the best number of comment pages. Finally, linear regression model, neural network model and support vector machine regression model are used to analyze the relationship between emotional tendency intensity and sales volume by predicting sales volume.
【學(xué)位授予單位】：東華大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F724.6;TP18

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