基于中文微博的產(chǎn)品評價(jià)分類及推薦算法研究
發(fā)布時(shí)間：2018-10-16 17:05
【摘要】：微博是近年新興的網(wǎng)絡(luò)媒體傳播平臺,它具有內(nèi)容簡短、傳播速度快、用戶眾多等特點(diǎn),而對于微博文本的情感分析是近年來數(shù)據(jù)挖掘的熱點(diǎn)之一,具有重要意義和價(jià)值。用戶在實(shí)施網(wǎng)上購物等行為時(shí),都希望從微博上獲取關(guān)注產(chǎn)品的評價(jià)信息。本文針對中文微博產(chǎn)品評價(jià)信息挖掘中存在的文本格式不規(guī)范、網(wǎng)絡(luò)用語大量使用、成分省略等文本特點(diǎn),及標(biāo)記數(shù)據(jù)稀缺、手工標(biāo)注困難等分類問題開展了如下幾項(xiàng)研究工作。 針對中文微博的文本特點(diǎn),提出了一種情感評價(jià)單元構(gòu)建方法。該方法分別構(gòu)建了情感評價(jià)詞、副詞和評價(jià)對象詞典,并制定了相應(yīng)的成分補(bǔ)充和單元構(gòu)建規(guī)則,不僅保證了提取信息的全面性和準(zhǔn)確性,還在精簡詞集、提高效率方面做出了嘗試。實(shí)驗(yàn)表明,該方法的準(zhǔn)確性比基于句法路徑的相關(guān)方法更高。 針對微博文本的分類問題,提出了一種基于圖半監(jiān)督學(xué)習(xí)的分類算法LP-SVM。該算法將標(biāo)簽擴(kuò)散過程與支持向量機(jī)相結(jié)合,不僅實(shí)現(xiàn)了少量標(biāo)記樣本的分類,而且避免了圖半監(jiān)督學(xué)習(xí)不產(chǎn)生分類器,對于新數(shù)據(jù)只能重新訓(xùn)練的問題。結(jié)合該算法對微博產(chǎn)品的情感評價(jià)單元進(jìn)行特征提取和半監(jiān)督分類。實(shí)驗(yàn)表明,該算法的表現(xiàn)優(yōu)于傳統(tǒng)及直推式的支持向量機(jī)算法。 結(jié)合實(shí)際應(yīng)用,提出了一種基于評價(jià)分類的微博產(chǎn)品推薦算法。該算法利用產(chǎn)品評價(jià)分類的結(jié)果,并結(jié)合微博的文本特征,制定了微博產(chǎn)品推薦指標(biāo)及其計(jì)算方法。實(shí)驗(yàn)最終得到的微博產(chǎn)品推薦方案與相關(guān)網(wǎng)站用戶評價(jià)結(jié)果基本一致,充分驗(yàn)證了該算法的準(zhǔn)確性。
[Abstract]:Weibo is a newly emerging network media communication platform in recent years. It has the characteristics of short content, fast communication speed, numerous users, etc. The emotional analysis of Weibo text is one of the hot spots of data mining in recent years, which has important significance and value. Users in the implementation of online shopping and other activities, they hope to obtain from Weibo concerned product evaluation information. In this paper, the text format of Chinese Weibo product evaluation information mining is not standard, the network language is used extensively, the composition is omitted and so on, and the marking data is scarce. The following research work has been carried out on the classification problems such as the difficulty of manual marking. According to the characteristics of Chinese Weibo, a method of constructing emotion evaluation unit is proposed. The method constructs the dictionary of emotion evaluation words, adverbs and evaluation objects, and formulates the corresponding component supplement and unit construction rules, which not only ensures the comprehensiveness and accuracy of extracting information, but also simplifies the word set. An attempt was made to improve efficiency. Experiments show that the proposed method is more accurate than the syntactic path-based correlation method. Aiming at the problem of Weibo text classification, this paper proposes a classification algorithm LP-SVM. based on graph semi-supervised learning. The algorithm combines tag diffusion process with support vector machine (SVM), which not only realizes the classification of a small number of labeled samples, but also avoids the problem of not producing classifiers in graph semi-supervised learning, so that the new data can only be retrained. Based on this algorithm, the feature extraction and semi-supervised classification of Weibo product emotion evaluation unit are carried out. Experimental results show that the proposed algorithm is superior to the traditional and direct push support vector machine (SVM) algorithms. Combined with practical application, a Weibo product recommendation algorithm based on evaluation and classification is proposed. Based on the results of product evaluation and classification and Weibo's text features, the proposed algorithm formulates the recommended product index and its calculation method. The experimental results of Weibo product recommendation are basically consistent with the results of user evaluation of related websites, which fully verify the accuracy of the algorithm.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.092
本文編號：2275057
[Abstract]:Weibo is a newly emerging network media communication platform in recent years. It has the characteristics of short content, fast communication speed, numerous users, etc. The emotional analysis of Weibo text is one of the hot spots of data mining in recent years, which has important significance and value. Users in the implementation of online shopping and other activities, they hope to obtain from Weibo concerned product evaluation information. In this paper, the text format of Chinese Weibo product evaluation information mining is not standard, the network language is used extensively, the composition is omitted and so on, and the marking data is scarce. The following research work has been carried out on the classification problems such as the difficulty of manual marking. According to the characteristics of Chinese Weibo, a method of constructing emotion evaluation unit is proposed. The method constructs the dictionary of emotion evaluation words, adverbs and evaluation objects, and formulates the corresponding component supplement and unit construction rules, which not only ensures the comprehensiveness and accuracy of extracting information, but also simplifies the word set. An attempt was made to improve efficiency. Experiments show that the proposed method is more accurate than the syntactic path-based correlation method. Aiming at the problem of Weibo text classification, this paper proposes a classification algorithm LP-SVM. based on graph semi-supervised learning. The algorithm combines tag diffusion process with support vector machine (SVM), which not only realizes the classification of a small number of labeled samples, but also avoids the problem of not producing classifiers in graph semi-supervised learning, so that the new data can only be retrained. Based on this algorithm, the feature extraction and semi-supervised classification of Weibo product emotion evaluation unit are carried out. Experimental results show that the proposed algorithm is superior to the traditional and direct push support vector machine (SVM) algorithms. Combined with practical application, a Weibo product recommendation algorithm based on evaluation and classification is proposed. Based on the results of product evaluation and classification and Weibo's text features, the proposed algorithm formulates the recommended product index and its calculation method. The experimental results of Weibo product recommendation are basically consistent with the results of user evaluation of related websites, which fully verify the accuracy of the algorithm.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.092
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 肖建鵬;張來順;任星;;直推式支持向量機(jī)在Web信息抽取中的應(yīng)用研究[J];計(jì)算機(jī)工程與應(yīng)用;2009年02期
2 劉志明;劉魯;;基于機(jī)器學(xué)習(xí)的中文微博情感分類實(shí)證研究[J];計(jì)算機(jī)工程與應(yīng)用;2012年01期
3 周立柱;賀宇凱;王建勇;;情感分析研究綜述[J];計(jì)算機(jī)應(yīng)用;2008年11期
4 楊經(jīng);林世平;;基于SVM的文本詞句情感分析[J];計(jì)算機(jī)應(yīng)用與軟件;2011年09期
5 朱嫣嵐;閔錦;周雅倩;黃萱菁;吳立德;;基于HowNet的詞匯語義傾向計(jì)算[J];中文信息學(xué)報(bào);2006年01期
6 章劍鋒;張奇;吳立德;黃萱菁;;中文觀點(diǎn)挖掘中的主觀性關(guān)系抽取[J];中文信息學(xué)報(bào);2008年02期
7 韓忠明;張玉沙;張慧;萬月亮;黃今慧;;有效的中文微博短文本傾向性分類算法[J];計(jì)算機(jī)應(yīng)用與軟件;2012年10期
8 王文遠(yuǎn);王大玲;馮時(shí);李任斐;王琳;;一種面向情感分析的微博表情情感詞典構(gòu)建及應(yīng)用[J];計(jì)算機(jī)與數(shù)字工程;2012年11期
9 周勝臣;瞿文婷;石英子;施詢之;孫韻辰;;中文微博情感分析研究綜述[J];計(jì)算機(jī)應(yīng)用與軟件;2013年03期
10 張珊;于留寶;胡長軍;;基于表情圖片與情感詞的中文微博情感分析[J];計(jì)算機(jī)科學(xué);2012年S3期
，本文編號：2275057
本文鏈接：http://sikaile.net/guanlilunwen/ydhl/2275057.html
最近更新
教材專著
