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  • 學位論文

財經領域情緒辭典之建置與其有效性之驗證-以財經新聞為元件

Creating and Verifying Sentiment Dictionary of Finance and Economics via Financial News

指導教授 : 陳國泰

摘要


近年來眾多研究探討文字資訊(如公司年報致股東報告書、新聞報導、分析師報告等)對於投資人決策之影響。這些研究所採取之方法中,內容分析法為最主要者之一。 內容分析法多根據情緒詞典衡量文字資訊所隱含之悲觀或樂觀情緒(sentiment),因此良好之情緒詞典實為文字資訊分析最之根本。然而檢視當前之中文情緒辭典,缺乏專門針對財經領域所編撰。本研究之目的即在建置一專門應用於財經領域之中文情緒辭典。 本研究以Loughran and McDonald所建置之財經領域英文情緒辭典為基礎,首先將其翻譯成中文,並以電腦比對中文新聞進行篩選。同時,進一步以群組共識之方式擴編與刪減,編製成中文財經領域之情緒辭典。 為驗證此一中文辭典之有效性,本研究進一步選取兩百篇新聞報導,分別以本研究所建立之辭典與台大資工系情緒辭典,衡量這些新聞報導的情緒分數,並以之與股票之表現進行相關與迴歸分析。結果發現,依據本研究所建立之辭典確實可以有效衡量新聞報導所隱含之樂(悲)關情緒。

並列摘要


Many studies have investigated the effect of textual information (e.g. MD&A, news reports, analyst reports) on investors’ decision. One of the most important methods adopted by these studies is content analysis. Content analysis usually tries to measure the sentiment contained in textual information based on some sentiment dictionary; therefore a good sentiment dictionary is the foundation for content analysis. However, currently there is no such Chinese dictionary in the area of economics and finance. This study intends to fill this gap by building a Chinese economic/finance sentiment dictionary. Our dictionary is based on the Englished economic/finance sentiment dictionary build by Loughran and McDonald. We first translate the English economic/finance sentiment dictionary into Chinese. We the use computer to calculate the number of times each word appear in 100 selected news reports. We further manually extend and remove some words by using a group consensus method. In order to verify the validity of our dictionary, we select 200 news reports as a sample and measure the sentiment of these news reports using our dictionary and the dictionary built by the department of computer science at National Taiwan Universituy (NTU). We then use correlation and regression analyses to investigate the relationship between the sentiment scores and stock performance. The analysis results show that our dictionary can effectively measure the sentiment of news report and outperforms the NTU dictionary.

參考文獻


莊友良、曾建勛、陳君銘、李英宗、王毅新,2012,「基於文字探勘技術的本體論學習方法研究—以理財相關報導為例」,長庚科技學刊,17期,頁39-52。
游正和、黃挺豪、陳信希,2012,「領域相關詞彙極性分析及文件情緒分類之研
周濟群、戚玉樑、曾建勛,2012,「以詞彙表為基礎的知識本體雛型建構研究—以「公司治理」領域知識為例」,圖書資訊學研究,6卷2期,頁37-81
Bennett, J. A., Sias, R. W. and Starks, L. T. 2003. Greener pastures and the impact of dynamic institutional preferences, Review of Financial Studies, 16(4): 1203–1238.
Bracewell, D. B. 2008. Semi-Automatic Creation of an Emotion Dictionary Using WordNet and its Evaluation. Proceedings of the IEEE conference on Cybernetics and Intelligent Systems, 21-24 .

被引用紀錄


李嘉洲(2016)。應用深度學習於財經新聞來源對股價趨勢預測之研究〔碩士論文,淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2016.00960
王釗東(2017)。以大數據探究財經新聞對台灣股票市場表現之影響〔碩士論文,國立臺灣大學〕。華藝線上圖書館。https://doi.org/10.6342/NTU201800089
陳昱豪(2017)。考慮投資人情緒下新聞對成交量之影響〔碩士論文,國立臺灣大學〕。華藝線上圖書館。https://doi.org/10.6342/NTU201701255

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