【摘要】：隨著互聯(lián)網(wǎng)的快速發(fā)展,人工智能逐漸獲得了人們更廣泛的關(guān)注,其研究也在人們的不斷努力下得到突破,進(jìn)入了新的發(fā)展階段。在此背景下,與其息息相關(guān)的情感分析相關(guān)研究工作也紛紛展開(kāi)。本文的核心是:①通過(guò)分析現(xiàn)有文本情感分析方法,對(duì)比多種傳統(tǒng)機(jī)器學(xué)習(xí)模型的情感分類效果;②引入集成學(xué)習(xí)方法,提出“多特征多分類器的元集成情感分析模型(MFMB-ME,Multi-Features Multi-Base-Classifiers Meta Ensemble Learning Sentiment Analysis Model)”,通過(guò)選用不同特征集與基分類器組合進(jìn)行集成學(xué)習(xí)模型訓(xùn)練,分析其情感分類效果,進(jìn)行情感分析實(shí)驗(yàn)并得出結(jié)論:通過(guò)使用MFMB-ME相比基于單特征集的集成分類模型,分類正確率能獲得明顯提升;③結(jié)合文本情感分析和態(tài)勢(shì)感知理論研究,總結(jié)出“文本情感分析的態(tài)勢(shì)感知模型(SA-SA,Sentiment Analysis Based On Situational Awareness Model)”。本文首先介紹了人工智能的發(fā)展并引出文本情感分析的意義與價(jià)值,通過(guò)對(duì)國(guó)內(nèi)外研究現(xiàn)狀的分析了解當(dāng)前文本情感分析的發(fā)展情況以及存在的問(wèn)題。詳細(xì)介紹了文本情感分析相關(guān)內(nèi)容及其現(xiàn)存流行技術(shù)。結(jié)合態(tài)勢(shì)感知方法論分析“文本情感分析的態(tài)勢(shì)感知模型”。其次,采用三次實(shí)驗(yàn)分別對(duì)比了基于傳統(tǒng)機(jī)器學(xué)習(xí)算法,單特征集成學(xué)習(xí)算法,“多特征集多基分類器的元學(xué)習(xí)集成學(xué)習(xí)”算法的文本情感分類效果�；趥鹘y(tǒng)機(jī)器學(xué)習(xí)方法實(shí)驗(yàn)中,分別采用決策樹(shù),支持向量機(jī),邏輯回歸算法進(jìn)行分類建模,對(duì)比分析不同模型的情感分類結(jié)果;基于單特征集成學(xué)習(xí)算法實(shí)驗(yàn)中,采用隨機(jī)森林對(duì)詞特征集進(jìn)行模型訓(xùn)練,對(duì)比分析其與傳統(tǒng)機(jī)器學(xué)習(xí)算法的分類效果差異;基于多特征集多分類器的元學(xué)習(xí)集成學(xué)習(xí)實(shí)驗(yàn)中,組合不同的文本特征集(包括詞,詞干,詞性,語(yǔ)法,n-gram等)與不同的基分類器(包括邏輯回歸,語(yǔ)言模型等),通過(guò)以隨機(jī)森林為元學(xué)習(xí)器的集成學(xué)習(xí)方法,對(duì)比分析不同組合策略的分類效果。最后綜合實(shí)驗(yàn)結(jié)果可分析:對(duì)于實(shí)驗(yàn)語(yǔ)料,與單特征集成學(xué)習(xí)分類模型和傳統(tǒng)機(jī)器學(xué)習(xí)分類模型相比較,本文提出的FMB-ME模型對(duì)測(cè)試集的分類正確率更高,具有更優(yōu)的分類效果,且分類性能提升較明顯。
[Abstract]:With the rapid development of the Internet, artificial intelligence has gradually gained more and more attention, and its research has also been broken through with the continuous efforts of people, and entered a new stage of development. Under this background, the related research work of affective analysis which is closely related to it is also carried out one after another. The core of this paper is the introduction of integrated learning method by analyzing existing text affective analysis methods and comparing the affective classification effects of many traditional machine learning models. This paper presents a multi-feature and multi-classifier Multi-Base-Classifiers Meta Ensemble Learning Sentiment Analysis Model) (MFMB-MEM Multi-Features Multi-Base-Classifiers Meta Ensemble Learning Sentiment Analysis Model). By using different feature sets and base classifiers to train the integrated learning model, the effect of emotion classification is analyzed. Through the experiment of emotion analysis, it is concluded that compared with the ensemble classification model based on single feature set with MFMB-ME, the classification accuracy can be significantly improved by combining text emotion analysis and situational awareness theory research. The SA-SAN sentiment Analysis Based On Situational Awareness Model) is summarized. This paper first introduces the development of artificial intelligence and elicits the significance and value of text emotional analysis, through the analysis of the current research situation at home and abroad to understand the current development of text emotional analysis and the existing problems. This paper introduces the related contents of text emotion analysis and the existing popular technology in detail. The situational perception model of text affective analysis combined with situational perception methodology. Secondly, three experiments are used to compare the effect of text emotion classification based on traditional machine learning algorithm, single feature ensemble learning algorithm and meta-learning ensemble learning algorithm based on multi-basis classifier. In the experiment based on traditional machine learning method, decision tree, support vector machine and logical regression algorithm are used to classify and model, and the results of emotion classification of different models are compared and analyzed. The model training of the word feature set is carried out by using random forest, and the classification effect is compared with the traditional machine learning algorithm. In the meta-learning ensemble learning experiment based on multi-classifier, different text feature sets (including words) are combined. Stem, part of speech, grammatical n-gram, etc.) and different base classifiers (including logical regression, language model, etc.). The classification effects of different combination strategies are compared and analyzed by using the integrated learning method using random forest as meta-learning device. Finally, the experimental results can be analyzed: for the experimental corpus, compared with the single feature integrated learning classification model and the traditional machine learning classification model, the FMB-ME model proposed in this paper has higher classification accuracy and better classification effect on the test set. And the classification performance is improved obviously.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.1

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