【摘要】：情感分類的主要目的是預測用戶在互聯(lián)網(wǎng)中發(fā)布情緒數(shù)據(jù)的極性(積極的或者消極的),各種語言的情感分析已經(jīng)成為諸多應用的研究熱點,然而由于不同語言的情感資源在質(zhì)量和數(shù)量上的不平衡,通常使用源語言來改善目標語言的跨語言情感分類方法,來提高目標語言情感分類的準確性.傳統(tǒng)的跨語言情感分類主要是通過機器翻譯將目標語言映射到源語言中,但是分類的準確性嚴重受到機器翻譯質(zhì)量的影響.通過對跨領域文本分類的結(jié)構學習算法(SCL)的討論和拉普拉斯映射對兩種語言之間詞對的影響,對跨語言結(jié)構對應學習算法(CLSCL)的改進,進而提出M-CLSCL算法,借助選出來的軸心詞對來進行目標語言的情感分類,通過M-CLSCL方法與前述相關方法的實驗結(jié)果進行比較,可以發(fā)現(xiàn)M-CLSCL提高了情感分類的準確性.
[Abstract]:The main purpose of affective classification is to predict the polarity (positive or negative) of emotional data published by users on the Internet. Emotional analysis of various languages has become a hot research topic in many applications. However, due to the imbalance between the quality and quantity of emotional resources in different languages, the source language is usually used to improve the cross-language affective classification method of the target language to improve the accuracy of the target language affective classification. The traditional cross-language affective classification mainly uses machine translation to map the target language to the source language, but the accuracy of classification is seriously affected by the quality of machine translation. By discussing the structure learning algorithm (SCL) of cross-domain text classification and the effect of Laplace mapping on the word pairs between two languages, the paper improves the cross-language structure corresponding learning algorithm (CLSCL), and then proposes the M-CLSCL algorithm. With the help of the selected axis word pairs to carry on the emotion classification of the target language, we can find that M-CLSCL improves the accuracy of the emotion classification by comparing the experimental results of the M-CLSCL method with the previous related methods.
【作者單位】： 河北工業(yè)大學計算機科學與軟件學院;河北省大數(shù)據(jù)計算重點實驗室(河北工業(yè)大學);
【基金】：天津市自然科學基金(14JCYBJC18500) 天津市應用基礎與前沿技術研究計劃(13JCQNJC00200)
【分類號】：TP391.1
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本文編號：2307240