A Stock Recommender System based on Text Mining and Machine Learning

碩士 === 國立屏東科技大學 === 資訊管理系所 === 102 === The data loading of internet keeps growing with the evolution of information technology, these data have lots of implied knowledge, but unstructured type and noise could be a problem, so we study to refine "Text Mining" for analysis process. Consider...

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Bibliographic Details
Main Authors:	Hsiao, Wei-Yuan, 蕭為元
Other Authors:	Chen, Deng-Neng
Format:	Others
Language:	zh-TW
Published:	2013
Online Access:	http://ndltd.ncl.edu.tw/handle/07997832880015939114

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spelling	ndltd-TW-102NPUS53960012016-12-22T04:18:36Z http://ndltd.ncl.edu.tw/handle/07997832880015939114 A Stock Recommender System based on Text Mining and Machine Learning 應用文字探勘及機器學習技術於股票推薦系統之研究 Hsiao, Wei-Yuan 蕭為元碩士國立屏東科技大學資訊管理系所 102 The data loading of internet keeps growing with the evolution of information technology, these data have lots of implied knowledge, but unstructured type and noise could be a problem, so we study to refine "Text Mining" for analysis process. Considering about quality and quantity of data, valid convenience, and time cost, we subject financial news to predict the trend of stock market for research, through HTML parser and CKIP for bag-of-words processing and WEKA for machine learning. Base on the importance of document itself, we propose a context oriented feature assign method by fluctuation of each stock, and investigate the feasibility by sampling test. Chen, Deng-Neng 陳灯能 2013 學位論文 ; thesis 64 zh-TW
collection	NDLTD
language	zh-TW
format	Others
sources	NDLTD
description	碩士 === 國立屏東科技大學 === 資訊管理系所 === 102 === The data loading of internet keeps growing with the evolution of information technology, these data have lots of implied knowledge, but unstructured type and noise could be a problem, so we study to refine "Text Mining" for analysis process. Considering about quality and quantity of data, valid convenience, and time cost, we subject financial news to predict the trend of stock market for research, through HTML parser and CKIP for bag-of-words processing and WEKA for machine learning. Base on the importance of document itself, we propose a context oriented feature assign method by fluctuation of each stock, and investigate the feasibility by sampling test.
author2	Chen, Deng-Neng
author_facet	Chen, Deng-Neng Hsiao, Wei-Yuan 蕭為元
author	Hsiao, Wei-Yuan 蕭為元
spellingShingle	Hsiao, Wei-Yuan 蕭為元 A Stock Recommender System based on Text Mining and Machine Learning
author_sort	Hsiao, Wei-Yuan
title	A Stock Recommender System based on Text Mining and Machine Learning
title_short	A Stock Recommender System based on Text Mining and Machine Learning
title_full	A Stock Recommender System based on Text Mining and Machine Learning
title_fullStr	A Stock Recommender System based on Text Mining and Machine Learning
title_full_unstemmed	A Stock Recommender System based on Text Mining and Machine Learning
title_sort	stock recommender system based on text mining and machine learning
publishDate	2013
url	http://ndltd.ncl.edu.tw/handle/07997832880015939114
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A Stock Recommender System based on Text Mining and Machine Learning

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