多次 LDA 主題模型及文本探勘應用於多種中文語料

Abstract

現今的文本資料數量越來越大，文字探勘分析也越趨重要。社會科學學者需要從大量資料中擷取資訊，並從大量文本中執行相對應的研究，因而衍伸出數位人文研究領域。本研究主要目的為藉由主題模型來找出文本內主要由哪一些主題構成；並對有興趣的主題之下的資訊進行文字探勘，並提供視覺化結果，從而幫助人文學者有效率得進行相關領域的研究。\n針對主題模型，我們提出了多次LDA的方法，亦針對不同語料，嘗試並比較不同主題模型，結果顯示多次LDA有效提升了主題模型的效果。\n本研究以輔助社會科學學者研究為出發點，藉由比較不同主題分析模型，使用者得以選擇效果較佳的模型並快速得知文本內的主要主題。另外使用者可以將這些有興趣的文本，進一步進行相關分析，減少學者閱讀大量文本所需耗費之時間。\n我們利用《人民大學期刊資料庫》、《維基百科》作為實驗與測試的中文語料，且將分析後的結果提供給學者，作為分析詮釋的參考資訊與佐證依據。

Nowadays, the number of textual materials is getting larger and larger, and text exploration and analysis are becoming more and more important. Social science scholars need to extract information from a large amount of data and perform corresponding research from a large amount of text, thus extending the field of Digital Humanities research. The main purpose of this research is to find out which topics are mainly composed of the text through topic modeling; to conduct text mining of the information under the topics of interest, and to provide visualization results, so as to help the humanities scholars to efficiently conduct research in related fields.\nThis research starts from assisting the research of social science scholars. By comparing different topic analysis models, users can choose the best model and quickly learn the main topics in the text. In addition, users can further analyze these interesting texts, reducing the time it takes for scholars to read a large number of texts.\nFor topic models, we proposed the multiple LDA methods. We also tried and compared different topic models with different corpora. The result shows that multiple LDA effectively improved the effect of topic models.\nWe use "Renmin University Journal Database" and "Wikipedia" as the Chinese corpus for experiments and tests, and provide the analysis results to scholars as reference information and supporting evidence for analysis and interpretation.\n