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研究生： 張祖耀
Chang, Tsu-Yao
論文名稱： 社群媒體訊息之資訊過濾與檢索
Information Filtering and Retrieval of Social Media Messages
指導教授： 鄧維光
Teng, Wei-Guang
學位類別： 碩士
Master
系所名稱： 工學院 - 工程科學系
Department of Engineering Science
論文出版年： 2017
畢業學年度： 105
語文別： 英文
論文頁數： 33
中文關鍵詞： 資訊過濾 、資訊檢索 、社群媒體 、短文分類
外文關鍵詞： information filtering, information retrieval, social media, short-text classification
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    • 隨著 Web 2.0 之觀念愈見普及至眾多的使用者，社群媒體之影響力益發重要，而由使 用者所撰寫的訊息量也不斷地以幾何速率增加，與論壇、評論、手機簡訊和電子郵件 相似，社群媒體上的訊息內容通常較短，往往僅包含數十個字，這些短文之數據結構 單純但卻即時，在許多應用情境中會有高度價值，因此如何提高資訊過濾和檢索的效 能已成為眾多研究工作之重點。與篇幅較長的文字資料相比，在進行短文資料之特徵 選取步驟時，便會遇到一些技術挑戰，諸如:特徵空間稀疏而難以充分利用文字特徵 之間的相關性、不同特徵造成後續分類結果差異很大等。在本研究中，我們提出並實 作了一套完整的系統流程，適切地結合特徵選取、資料分類與分群技術，以實現網路 媒體訊息之資訊過濾與檢索工作。

    With the wide spread of the Web 2.0 concept, the influence of social media is becoming more and more important. In addition, the amount of messages generated by numerous users grows at a geometric rate. These messages usually contain only tens of words that are analogous to forum articles, user reviews, short messages and e-mails. These short-text data are of simple structure but may contain real-time information, indicating their potential values in many practical applications. Consequently, the way of improving the performance of corresponding information filtering and retrieval tasks has become the focus of many research works. Note that conducting the feature selection step on short-text data is more challenging than that on long text data. Possible problems include difficulties to make full use of the correlation between the features due to the sparse feature space and varying impacts of obtaining different classification results based on different features. In this work, we thus propose and implement a general scheme that incorporates feature selection, data classification and clustering techniques. With this carefully devised scheme, it is verified that information filtering and information retrieval tasks can be successfully accomplished.

    Chapter 1 Introduction.......................................1 1.1 Motivation and Overview....................................1 1.2 Contributions of This Work..............................2 Chapter 2 Preliminaries..........................................3 2.1 Extracting Messages from Social Media.....................3 2.1.1 Basics of Social Media.................................3 2.1.2 Characteristics of Short-text from Social Media............4 2.1.3 Challenges of Handling Short-text........................5 2.2 General Flows of Short-text Classification..................6 2.2.1 Preprocessing of Short-text Documents..................7 2.2.2 Short-text Feature Construction and Selection............8 2.2.3 Short-text Classification.................................9 Chapter 3 Proposed Approach for Short-text Filtering and Retrieval.........11 3.1 Data Sources and Preprocessing..........................................11 3.2 Short-text Feature Selection.............................................13 3.3 Proposed Scheme of Information Filtering and Retrieval..................16 3.3.1 Training Data Selection and Process Flow..............................16 3.3.2 Usage of Latent Dirichlet Allocation.................................17 3.3.3 Usage of Support Vector Machines....................................18 Chapter 4 Empirical Studies.....................19 4.1 Experimental Environment....................19 4.2 Experimental Process........................20 4.3 Experimental Results........................21 4.3.1 Datasets..................................21 4.3.2 Classification Using SVM..................22 4.3.3 Clustering Using LDA......................26 Chapter 5 Conclusions and Future Works.......29 Bibliography.................................30

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