近年來,情感分析一直是自然語言處理研究者群體日益關(guān)注的主題。情感分析可以幫助公司和公共管理部門的人員更多地了解客戶的意見,并幫助他們做出一些決定。在本文中,我們首先介紹了情感分析任務(wù)的背景,定義,以便讀者更好地理解本文的研究目標(biāo)以及論文的貢獻(xiàn)。我們還介紹了最近的幾種情感分析的方法,如概率算法（樸素貝葉斯）,最近鄰算法和變量算法,決策樹或分類和矢量支持機(jī)器。然后介紹了構(gòu)建情感分析系統(tǒng)的步驟,包括預(yù)處理,特征提取和性能評估。最后,我們更加關(guān)注由在線酒店評論組成的數(shù)據(jù)集,并應(yīng)用監(jiān)督機(jī)器學(xué)習(xí)方法Na?ve Bayes使用unigram特征和兩種類型的信息（頻率和TF-IDF）來實(shí)現(xiàn)文檔的極性分類。如我們的實(shí)驗(yàn)結(jié)果所示,在準(zhǔn)確性,精確度,召回率和F_score方面,我們的模型優(yōu)于其他模型。 

【文章頁數(shù)】：62 頁

【學(xué)位級別】：碩士

【文章目錄】：
摘要
Abstract
List of abreviations
Chapter Ⅰ Introduction
    1.1.Research Background
    1.2.Research Significance
    1.3.Research Status
    1.4.My Contribution
    1.5.Thesis Outline
Chapter Ⅱ Related Work
    2.1.Sentiment Analysis
        2.1.1.Definitions,Tasks,and Terminology
        2.1.2.Opinion Sentiment Analysis
    2.2.Sentimental Analysis Customer Review
        2.2.1.Sources for Online Reviews
        2.2.2.Formats of Online Reviews
        2.2.3.Specifics of the Application Domain
        2.2.4.Subtasks in Sentimental Analysis Customer Review
    2.3.Text classification approaches
        2.3.1 Probabilistic Algorithms(Naive Bayes)
        2.3.2 Algorithm of the Nearest Neighbor and Variants
        2.3.3 Decision Trees or Classification
        2.3.4 Vector Support Machines
Chapter Ⅲ Sentimental Analysis System Construction
    3.1.Approaches
    3.2.Preprocessing
    3.3.Feature extraction
    3.4.Evaluation Metrics
    3.5.Visualize Results
Chapter Ⅳ Methods and Results
    4.1 Methods
        4.1.1 Term frequency-inverse document frequency(tf-idf)
        4.1.2.Naive Bayes Algorithm
    4.2.Experimental Results
        4.2.1.Dataset
        4.2.2.Experimental Setting
        4.2.3.Evaluation Metric
        4.2.4.Experimental Results
        4.2.5.Results of Evaluation Metrics
Chapter Ⅴ Conclusions and Future Work
    5.1 Conclusions
    5.2 Future Work
攻碩士學(xué)位期間取得的研究成果
Acknowledgement
References
附件



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