本文選題：情感分類 + 跨領(lǐng)域�。� 參考：《重慶大學(xué)》2016年碩士論文

【摘要】：隨著電子商務(wù)的快速發(fā)展和微博、微信等的崛起,互聯(lián)網(wǎng)上的短文本評論呈指數(shù)形式地增長,這些評論信息的背后蘊藏著巨大的經(jīng)濟和社會價值。傳統(tǒng)的手工處理方法變得越來越困難,如何自動化地挖掘這些評論中的有用信息是自然語言處理領(lǐng)域的一個研究熱點。文本情感分類技術(shù)應(yīng)運而生,而跨領(lǐng)域情感分類由于不需要目標(biāo)領(lǐng)域標(biāo)記評論,實用性更強。情感分類作為一種主觀的文本挖掘技術(shù),其目的是判斷評論者對某實體(產(chǎn)品、服務(wù)、事件等)的情感傾向和評價態(tài)度(正面或負(fù)面、推薦或不推薦等)。在對現(xiàn)有情感分類算法和相關(guān)技術(shù)進行了深入的研究基礎(chǔ)上,提出了自己的跨領(lǐng)域情感分類算法。主要研究成果如下:(1)提出了基于情感敏感性詞庫(Sentiment Sensitive Thesaurus,SST)的跨領(lǐng)域情感分類算法。針對跨領(lǐng)域分類中原始領(lǐng)域()和目標(biāo)領(lǐng)域()的領(lǐng)域獨立性問題,提出構(gòu)建SST詞庫,然后利用SST詞庫對原始領(lǐng)域和目標(biāo)領(lǐng)域的評論集進行特征向量擴展,最后利用擴展之后的評論集進行分類器訓(xùn)練和分類預(yù)測。SST是在和的評論集上構(gòu)建的,同時包含兩類領(lǐng)域的特征。該算法利用支持向量機(SVM)對擴展之后的原始領(lǐng)域評論集進行分類器的訓(xùn)練,所得分類器對擴展之后的目標(biāo)領(lǐng)域評論集進行分類預(yù)測。通過在酒店、電腦和書籍三個領(lǐng)域的數(shù)據(jù)集上進行9組實驗表明,基于SST的跨領(lǐng)域分類算法分類效果較好。論文還對算法中的參數(shù)K和訓(xùn)練集大小對分類器分類效果的影響進行了實驗探討。(2)提出了投票集成的跨領(lǐng)域情感分類算法。利用集成學(xué)習(xí)的思想組合多個基分類器的結(jié)果來提升分類器分類效果。實驗中采用了簡單投票和加權(quán)投票兩種方式,同樣在酒店、電腦和書籍三個語料庫上進行實驗,結(jié)果表明投票集成分類算法分類效果明顯優(yōu)于單個基分類器的分類效果。(3)改進的Stacking集成分類算法。算法利用無監(jiān)督的NTUSD情感詞典分類方法,先對目標(biāo)領(lǐng)域評論集進行分類,將其中部分情感極性較強的評論進行標(biāo)記后加入到原始領(lǐng)域的評論集中,擴展訓(xùn)練集的構(gòu)成,減小領(lǐng)域差異性。通過這種方式改進Stacking算法在跨領(lǐng)域分類中的實際應(yīng)用效果。實驗結(jié)果表明,Stacking集成分類算法能獲得較好的分類效果,集成學(xué)習(xí)在跨領(lǐng)域情感分類中的應(yīng)用具有研究價值。
[Abstract]:With the rapid development of electronic commerce and the rise of Weibo and WeChat, the short text reviews on the Internet have increased exponentially. Behind these comments, there are enormous economic and social values. Traditional manual processing methods are becoming more and more difficult. How to automatically mine useful information from these comments is a research hotspot in the field of natural language processing. The technology of text emotion classification emerges as the times require, and cross-domain emotion classification is more practical because it does not need target domain tagging comment. As a subjective text mining technique, emotion classification aims to judge the emotional tendency and evaluation attitude (positive or negative, recommendation or not) of the reviewer towards a certain entity (product, service, event, etc.). On the basis of deep research on the existing emotion classification algorithms and related technologies, this paper puts forward its own cross-domain emotion classification algorithm. The main results are as follows: (1) A cross-domain emotion classification algorithm based on sentiment sensitive Thesaurus (SST) is proposed. Aiming at the problem of domain independence of original domain () and target domain () in cross-domain classification, this paper proposes to construct SST lexicon, and then extends the comment set of original domain and target domain by using SST lexicon. Finally, the extended comment set is used for classifier training and classification prediction. The SST is constructed on the comment set of the sum and contains two kinds of domain features. Support vector machine (SVM) is used to train the original domain comment set, and the classifier is used to predict the extended target domain comment set. Nine groups of experiments on the data sets of hotel, computer and books show that the algorithm based on SST is effective. The effect of parameter K and training set size on classifier classification effect is also discussed experimentally. (2) A cross-domain emotion classification algorithm based on voting ensemble is proposed. Using the idea of integrated learning to combine the results of multiple base classifiers to improve the classifier classification effect. The experiment was conducted in two ways: simple voting and weighted voting. The experiments were also carried out on three corpora: hotel, computer and books. The results show that the classification effect of voting ensemble classifier is better than that of single base classifier. (3) improved Stacking ensemble classification algorithm. The algorithm uses the unsupervised NTUSD emotion dictionary classification method, classifies the target domain comment set first, marks some of the comments with strong affective polarity, then adds them to the original domain comment set to expand the composition of the training set. Reduce domain differences. In this way, the effect of Stacking algorithm in cross-domain classification is improved. The experimental results show that Stacking ensemble classification algorithm can achieve better classification effect, and the application of ensemble learning in cross-domain emotion classification is valuable.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.1

【參考文獻】

相關(guān)期刊論文 前6條

1 周星;丁立新;萬潤澤;葛強;;分類器集成算法研究[J];武漢大學(xué)學(xué)報(理學(xué)版);2015年06期

2 魏現(xiàn)輝;張紹武;楊亮;林鴻飛;;基于加權(quán)SimRank的跨領(lǐng)域文本情感傾向性分析[J];模式識別與人工智能;2013年11期

3 吳瓊;劉悅;沈華偉;張瑾;許洪波;程學(xué)旗;;面向跨領(lǐng)域情感分類的統(tǒng)一框架[J];計算機研究與發(fā)展;2013年08期

4 張慧;李壽山;李培峰;朱巧明;;基于評價對象類別的跨領(lǐng)域情感分類方法研究[J];計算機科學(xué);2013年01期

5 翟忠武;徐華;賈培發(fā);;An Empirical Study of Unsupervised Sentiment Classification of Chinese Reviews[J];Tsinghua Science and Technology;2010年06期

6 朱嫣嵐;閔錦;周雅倩;黃萱菁;吳立德;;基于HowNet的詞匯語義傾向計算[J];中文信息學(xué)報;2006年01期

相關(guān)博士學(xué)位論文 前2條

1 李巖;文本情感分析中關(guān)鍵問題的研究[D];北京郵電大學(xué);2014年

2 陳博;WEB文本情感分類中關(guān)鍵問題的研究[D];北京郵電大學(xué);2008年

相關(guān)碩士學(xué)位論文 前2條

1 華林森;中文文本情感分類研究[D];重慶大學(xué);2014年

2 徐帥;基于中文微博的情感分析研究[D];華中科技大學(xué);2013年

，

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