基于結(jié)構(gòu)對(duì)應(yīng)學(xué)習(xí)的跨語言情感分類研究
發(fā)布時(shí)間：2018-11-03 08:22
【摘要】：情感分類的主要目的是預(yù)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(yīng)用的研究熱點(diǎn),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(biāo)語言的跨語言情感分類方法,來提高目標(biāo)語言情感分類的準(zhǔn)確性.傳統(tǒng)的跨語言情感分類主要是通過機(jī)器翻譯將目標(biāo)語言映射到源語言中,但是分類的準(zhǔn)確性嚴(yán)重受到機(jī)器翻譯質(zhì)量的影響.通過對(duì)跨領(lǐng)域文本分類的結(jié)構(gòu)學(xué)習(xí)算法(SCL)的討論和拉普拉斯映射對(duì)兩種語言之間詞對(duì)的影響,對(duì)跨語言結(jié)構(gòu)對(duì)應(yīng)學(xué)習(xí)算法(CLSCL)的改進(jìn),進(jìn)而提出M-CLSCL算法,借助選出來的軸心詞對(duì)來進(jìn)行目標(biāo)語言的情感分類,通過M-CLSCL方法與前述相關(guān)方法的實(shí)驗(yàn)結(jié)果進(jìn)行比較,可以發(fā)現(xiàn)M-CLSCL提高了情感分類的準(zhǔn)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(xué)計(jì)算機(jī)科學(xué)與軟件學(xué)院;河北省大數(shù)據(jù)計(jì)算重點(diǎn)實(shí)驗(yàn)室(河北工業(yè)大學(xué));
【基金】：天津市自然科學(xué)基金(14JCYBJC18500) 天津市應(yīng)用基礎(chǔ)與前沿技術(shù)研究計(jì)劃(13JCQNJC00200)
【分類號(hào)】：TP391.1
，
本文編號(hào)：2307240
[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(xué)計(jì)算機(jī)科學(xué)與軟件學(xué)院;河北省大數(shù)據(jù)計(jì)算重點(diǎn)實(shí)驗(yàn)室(河北工業(yè)大學(xué));
【基金】：天津市自然科學(xué)基金(14JCYBJC18500) 天津市應(yīng)用基礎(chǔ)與前沿技術(shù)研究計(jì)劃(13JCQNJC00200)
【分類號(hào)】：TP391.1
，
本文編號(hào)：2307240
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