發(fā)布時(shí)間：2018-03-24 19:53

  本文選題：網(wǎng)絡(luò)評(píng)論　切入點(diǎn)：數(shù)據(jù)獲取　出處：《大連海事大學(xué)》2017年碩士論文

【摘要】：網(wǎng)絡(luò)商品評(píng)論情感分析主要分為粗粒度的情感分析與細(xì)粒度的情感分析,粗粒度的情感極性分析重在用于判斷文本整體的情感傾向,主要的方法特征項(xiàng)選擇、特征項(xiàng)降維和特征項(xiàng)權(quán)重計(jì)算。粗粒度的情感分析并沒(méi)有從屬性角度出發(fā)更詳細(xì)地在解決消費(fèi)者面臨的評(píng)論數(shù)據(jù)超載和識(shí)別過(guò)程復(fù)雜的問(wèn)題,而細(xì)粒度的情感極性分析重在從評(píng)論中找出這些商品屬性,并判斷消費(fèi)者對(duì)于各個(gè)屬性的情感傾向。為使情感分析的效用更加精準(zhǔn)的作用于某一屬性之上,并展示給大眾,本文利用網(wǎng)絡(luò)爬蟲(chóng)技術(shù)獲取網(wǎng)絡(luò)評(píng)論語(yǔ)料,然后將語(yǔ)料中的屬性詞與情感詞抽取出來(lái)進(jìn)行細(xì)粒度情感傾向分析與計(jì)算,并設(shè)計(jì)相應(yīng)的系統(tǒng)將實(shí)驗(yàn)分析與結(jié)果展示出來(lái)。本文主要研究工作為,首先利用Shark-PageRank主題爬取算法生成待爬取網(wǎng)頁(yè)隊(duì)列方法、Bloom Filter網(wǎng)頁(yè)去重方法和Jsoup網(wǎng)頁(yè)數(shù)據(jù)獲取方法三者相結(jié)合的網(wǎng)絡(luò)爬蟲(chóng)技術(shù)進(jìn)行數(shù)據(jù)獲取,將抓取到的網(wǎng)絡(luò)評(píng)論作為語(yǔ)料庫(kù)。然后結(jié)合中文網(wǎng)絡(luò)評(píng)論語(yǔ)言表達(dá)方式的特點(diǎn),使用一種基于語(yǔ)義分析的屬性詞與情感詞詞對(duì)聯(lián)合識(shí)別抽取的方法,并將該抽取方法與面向?qū)傩栽~的方法相結(jié)合提出了一種基于語(yǔ)義分析網(wǎng)絡(luò)評(píng)論細(xì)粒度情感分析方法。最后,在上述工作基礎(chǔ)上,結(jié)合Web前端技術(shù),設(shè)計(jì)了一個(gè)網(wǎng)絡(luò)評(píng)論細(xì)粒度情感分析系統(tǒng),此系統(tǒng)提供網(wǎng)絡(luò)評(píng)論數(shù)據(jù)獲取,網(wǎng)絡(luò)評(píng)論細(xì)粒度情感分析,并將評(píng)論文本的細(xì)粒度情感分析結(jié)果展示的功能。經(jīng)過(guò)對(duì)本論文的鉆研分析,可以將用戶(hù)評(píng)論的情感分析的精確性更深一步提升,并使其通過(guò)情感分析在商品屬性上進(jìn)行修整、制定消費(fèi)決策等兩方面發(fā)揮更大的指導(dǎo)作用。
[Abstract]:The emotional analysis of online commodity reviews is mainly divided into coarse-grained emotional analysis and fine-grained emotional analysis. Coarse-grained affective polarity analysis is mainly used to judge the emotional tendency of the text as a whole. Coarse-grained emotional analysis does not address in more detail the complexity of comment data overload and identification process faced by consumers from the perspective of attributes. The fine-grained affective polarity analysis focuses on finding out these commodity attributes from the comments and judging the consumer's emotional propensity for each attribute, in order to make the utility of emotional analysis more accurate on one attribute and show it to the public. In this paper, the web crawler technology is used to obtain the network comment corpus, and then the attribute words and emotion words from the corpus are extracted for fine-grained emotional tendency analysis and calculation. And design the corresponding system to display the experimental analysis and results. Firstly, the Shark-PageRank topic crawling algorithm is used to generate the web crawler technology which combines the bloom Filter web page removal method and the Jsoup web page data acquisition method to obtain the data. The collected comments are used as a corpus, and then a method based on semantic analysis is used to identify and extract attribute words and affective words according to the characteristics of the Chinese network comment language. A fine-grained emotional analysis method based on semantic analysis for network comments is proposed by combining the extraction method with the attribute-oriented method. Finally, based on the above work, combined with the Web front-end technology, A fine-grained emotional analysis system for network comments is designed. The system provides data acquisition for network reviews and fine-grained emotional analysis for network reviews. Through the research and analysis of this paper, we can further enhance the accuracy of the emotional analysis of user comments, and make it through emotional analysis to modify the commodity attributes. Making consumer decisions and other two aspects to play a greater guiding role.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.1

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