本文選題：文本情感傾向性分類 + 互信息��； 參考：《武漢大學(xué)學(xué)報(理學(xué)版)》2017年03期

【摘要】：為了提高文本情感傾向性分類的精度,提出了一種文本情感傾向性分析方法 bfsmPMI-SVM.該方法在文本預(yù)處理階段,濾除了對表述主題情感傾向性不強烈的語句以及無關(guān)停用詞等;用改進(jìn)的PMI-IR算法對情感傾向性詞語抽取,并自動擴(kuò)充了正負(fù)基準(zhǔn)詞集;改進(jìn)了互信息(MI)算法,在MI的計算中增加了詞頻因子(f)、類別差異因子(b)和符號因子(s).利用改進(jìn)的MI算法選擇文本特征,融合其他一些文本特征,用SVM實現(xiàn)文本情感傾向性分類.實驗以食品安全領(lǐng)域爬取文本為例,與PMI-IR-SVM和MI-SVM算法的傾向分析相比,本文方法的正向文本準(zhǔn)確率、負(fù)向文本準(zhǔn)確率、召回率和F1值等都有提高.
[Abstract]:In order to improve the accuracy of text affective orientation classification, a text affective orientation analysis method bfsmPMI-SVM is proposed. In the stage of text preprocessing, the method filters out sentences which are not strong in the tendency to express the subject emotion, and uses the improved PMI-IR algorithm to extract the affective preference words, and automatically expands the positive and negative reference words set. The mutual information Mi) algorithm is improved, and the word frequency factor, category difference factor and symbol factor are added in the calculation of MI. The improved MI algorithm is used to select text features and some other text features are fused, and SVM is used to realize text affective orientation classification. The experiment takes crawling text in the field of food safety as an example. Compared with the tendency analysis of PMI-IR-SVM and MI-SVM algorithms, the forward text accuracy, negative text accuracy, recall rate and F1 value of this method are improved.
【作者單位】： 武漢大學(xué)計算機(jī)學(xué)院;武漢大學(xué)國際軟件學(xué)院;
【基金】：國家自然科學(xué)基金資助項目(61303214,61672393,U1536204)
【分類號】：TP391.1
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本文編號：1857823