本文選題：評(píng)論文本 + 屬性因子��； 參考：《數(shù)據(jù)分析與知識(shí)發(fā)現(xiàn)》2017年10期

【摘要】：【目的】從評(píng)論對(duì)象的屬性特征出發(fā)解決情感極性量化問題�！痉椒ā繉⒃诰�評(píng)論文本分解構(gòu)建三層評(píng)論體系,即評(píng)論對(duì)象 對(duì)象屬性 評(píng)論描述,從屬性層級(jí)抽取屬性詞集和對(duì)應(yīng)的評(píng)論集,考慮評(píng)論對(duì)象屬性特征的不同影響,引入屬性因子,并對(duì)TFIDF進(jìn)行改進(jìn)用以計(jì)算屬性因子;結(jié)合評(píng)論模式和評(píng)論語境提出基于屬性特征的評(píng)論情感量化分析算法并采用Python語言予以實(shí)現(xiàn)�！窘Y(jié)果】相較于傳統(tǒng)機(jī)器學(xué)習(xí)分類算法(NB、SVM)、屬性因子設(shè)置為等權(quán)重時(shí),本文算法在評(píng)論文本情感分類準(zhǔn)確性方面有顯著提高。【局限】評(píng)論集領(lǐng)域選擇方面具有局限性,量化算法在系數(shù)設(shè)定方面存在主觀性�！窘Y(jié)論】本文算法能有效解決情感極性量化問題,進(jìn)一步提高了情感分類準(zhǔn)確性。
[Abstract]:[objective] to solve the problem of quantification of emotional polarity based on the attribute characteristics of comment objects. [methods] the online comment text is decomposed into three layers of comment system. The attribute word set and the corresponding comment set are extracted from the attribute level, considering the different effects of the attribute characteristics of the comment object, the attribute factor is introduced, and the TFIDF is improved to calculate the attribute factor. Combining the comment pattern with the comment context, a quantitative analysis algorithm of comment emotion based on attribute feature is proposed and implemented in Python language. [results] compared with the traditional machine learning classification algorithm (NBN SVM), when the attribute factor is set to equal weight, The algorithm in this paper has significantly improved the accuracy of emotional classification of comment text. [limitations] comment set domain selection has limitations, Quantization algorithm has subjectivity in coefficient setting. [conclusion] this algorithm can effectively solve the problem of emotional polarity quantization and further improve the accuracy of emotion classification.
【作者單位】： 西安電子科技大學(xué)經(jīng)濟(jì)與管理學(xué)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目“基于可信語義Wiki的知識(shí)庫構(gòu)建方法與研究應(yīng)用”(項(xiàng)目編號(hào):71203173) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金資助項(xiàng)目“大數(shù)據(jù)環(huán)境下基于主題模型的信息服務(wù)研究”(項(xiàng)目編號(hào):JB160606) 國(guó)家自然科學(xué)青年基金項(xiàng)目“大規(guī)模動(dòng)態(tài)社交網(wǎng)絡(luò)社團(tuán)檢測(cè)算法研究”(項(xiàng)目編號(hào):71401130)的研究成果之一
【分類號(hào)】：TP391.1

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