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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/81464
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/81464

    Title: 兩種中文情感運算分析策略: 以部首為基礎及深層類神經學習
    Two Chinese Sentiment Analysis Approaches: Radical-based and Deep Learning Neural Network
    Authors: 趙逢毅
    Chao, August F.Y.
    Contributors: 楊亨利
    Yang, Heng Li
    Chao, August F.Y.
    Keywords: 中文情感分析
    Chinese Sentiment Analysis
    Radical Information
    Deep Learning
    Feature Selection
    Feature Extraction
    Date: 2015
    Issue Date: 2016-03-01 10:27:35 (UTC+8)
    Abstract: 評論是所有人類行為的核心,因為它影響我們行為的關鍵因素。我們都試著從不同型式的評論分析與研究試著從作者字裡行間的文字呈現內容深入推敲及理解,從而要能過濾出能協助決策的有用資訊。在早期的評論研究將評論視為是文本分類問題,直到2000年前後,從分析評論的主觀句子與評論裡形容詞的程度衡量用詞,學者們開始對解構整篇文本的內容,並試著從語言學的角度分析用字遣詞與情感方向之間的關聯。這種從文字語義關聯分析評論的方式,也使文本挖掘技術必需結合自然語言的處理原則,才能更準確地了解評論的內容。隨著許多新興的機器學習演算法與自然語言處理方法不斷地推陳出新,及網路使用行為拓展至電子商務與線上虛擬社群的建立,情感分析研究亦開始不斷地蓬勃發展。
    Opinion is the core of human behaviors, because it directly influences key factor of our behaviors. Despite of personal or organizational decision making processes, we all constantly conduct various kinds of opinion analysis, including explaining and comprehending what users present. At the beginning, opinion studies considered as a text mining problems, and tried to cluster opinions into positive and negative groups. After 2000, researchers intended to decompose sentences from whole opinions by analysing subjective expressing and adjective words presenting within, as well as explained the relationships between semantics and sentiment from linguistics aspect. Therefore, opinion analysis has to incorporate with natural language processing techniques, so we can understand the opinion contents. Nowadays, sentiment analysis grows event booming due to emerging machine learning and natural language processing approaches, as well as the needs of electronic commerce and virtual community on line.
    Unfortunately, Chinese is quite unlike other language due to non-space separated, one character as one morpheme, and considering words (compositing with several characters) as minimum semantic expression unit. And those language features also bring difficult to adopted sentiment analysis principles from English. Nevertheless, researchers leveraged Chinese language information to propose specific sentiment analysis approaches dedicated to analyze Chinese opinions. In this study, we practically discussed the situations of conducting sentiment analysis: (a) using sentiment analysis resources and experts’ knowledge; and (b) using word feature vector, called word2vec, and deep learning. In (a) scenario, we propose a Chinese radical-based sentiment analysis approach and experiment the applicability. We also proposed a feature extraction method, so we can generate 50 seeds for further analysis. In (b), we compared 4 different feature selection approaches for deep learning, in order to keep accuracy and make sure understandable feature can be generated in neural network. We also tested feature selection approaches in SVM classifier and retrieved similar results. In this study, we also discussed essential constraints and required information in both scenarios, as well as the results of this study can be the foundation of continuing Chinese sentiment analysis studies.
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    Description: 博士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0973565061
    Data Type: thesis
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