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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 魏志平 | |
dc.contributor.author | Yu-An Chen | en |
dc.contributor.author | 陳昱安 | zh_TW |
dc.date.accessioned | 2021-06-16T02:25:20Z | - |
dc.date.available | 2015-08-11 | |
dc.date.copyright | 2015-08-11 | |
dc.date.issued | 2015 | |
dc.date.submitted | 2015-08-06 | |
dc.identifier.citation | Allan, J., Papka, R., & Lavrenko, V. (1998). On-line new event detection and tracking. Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval.
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/53520 | - |
dc.description.abstract | 在這個時代,經營環境已經變成高度競爭以及邁向全球化,企業經常發動積極的行動去獲得優勢位置或者產業上的獲利。因此,如何判斷市場時機去發動競爭活動就變得很重要。然而,在遭受競爭活動影響時,企業也會採取競爭性回應行動去回應對手的競爭性活動,削弱對手可取得的競爭優勢。但是,被動的回應可以選擇的行動就會受到限制,也不容易取得更好的競爭優勢,如果可以預測對手的下一步競爭活動,就有辦法及早做出預防行動,或提前破壞對手的計畫,進而奪取更加有利的競爭位置。
因此,本研究目的是建構一個預測系統,依據過去企業本身的競爭行動、市場上所有競爭者的競爭行動、以及企業直接競爭者的競爭行動,來預測該企業未來的競爭行動。考慮到系統的自動化,問卷方式的資料來源並不可行,因此我們決定採取擁有大量資料的新聞文件。本研究會說明如何取得文件以及文件的前處理,並且因為資料的不對稱性,我們提出一個解決方法,除此之外,我們也針對系統嘗試不同的研究。結果顯示,透過新聞文件是有辦法作預測模型,並且以次數的資料表達方式所產生的預測結果更好,最後,我們也發現不同的競爭活動需要考慮的面相不同,資料也應該選用不一樣的特徵。 | zh_TW |
dc.description.abstract | Due to highly competitive and globalized business environments, firms frequently take aggressive actions to challenge their competitors in an effort to gain advantageous position on their market or improve relative performance. Therefore, it is an important issue how to identify market opportunities and timing to initiate and develop effective competitive actions. However, when a firm is affected by competitors’ competitive actions, it will undertake competitive counteractions in response to competitors’ actions to destroy or weaken rivals’ competitive advantages. But, passive responses are limited and cannot easily gain competitive advantages. If a firm can predict its rivals’ future competitive actions, it can initiate some preventive actions early or even destroy its rivals’ plan in order for the firm to capture better competitive position. Therefore, the objective of this study is to build a prediction system. According to the earlier competitive actions of a focal firm itself, all competitors’ competitive actions in market, and its direct competitors’ competitive actions, our proposed system can predict the focal firm’s future competitive actions. To be automated, it is not feasible for our system to rely on questionnaire items as its predictors. We thus decide to exploit news documents, which are large in size. In addition, our prediction system also faces a class imbalance problem. Thus, we develop an ensemble approach to address this problem. Besides, we conduct several experiments to evaluate the effectiveness of our proposed system. Our experimental results show that our proposed prediction system can predict competitive actions on the basis of data extracted from news documents, and it will get better result when using frequency as the representation scheme. Finally, we find out that different competitive action types need to consider different types of predictors, so the proposed system should apply different feature sets for different types of competitive actions. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T02:25:20Z (GMT). No. of bitstreams: 1 ntu-104-R02725010-1.pdf: 1067875 bytes, checksum: 06af0b53c05d575175ec89fa0ed8b19b (MD5) Previous issue date: 2015 | en |
dc.description.tableofcontents | 誌謝 i
中文摘要 ii Abstract iii Contents v List of Figures vii List of Tables viii Chapter 1 Introduction 1 1.1 Background 1 1.2 Research Motivation 3 1.3 Research Objective 5 Chapter 2 Literature Review 7 2.1 Competitive Action and Dynamics 7 2.2 Text Mining on News Documents 10 Chapter 3 Our Proposed Method 12 3.1 Our Typology of Competitive Actions 12 3.2 Prediction Variables 16 3.2.1 Dimension of Variables 16 3.2.2 Time Window 17 3.2.3 Representation of Variables 17 3.2.4 Identifying Direct Competitors of Companies 18 3.2.5 Rate of Market Dependence 19 3.3 Learning Method 20 Chapter 4 Experiments 23 4.1 Data 23 4.1.1 Data Collection 23 4.1.2 Data Selection 23 4.1.3 Data Labeling 25 4.2 Evaluation Design 27 4.2.1 Design experiments 27 4.2.2 Features of Testing Data 28 4.2.3 Default Setting 29 Chapter 5 Results and Analysis 31 5.1 Overall Performance on the Base System 31 5.2 Robustness Checking 36 5.3 Effects of Feature Representation Schemes 38 5.4 Effects of Feature Granularity 40 Chapter 6 Conclusion 42 6.1 Conclusion 42 6.2 Limitation and Future work 43 REFERENCES 45 | |
dc.language.iso | en | |
dc.title | 從新聞文件中預測與分析企業競爭活動 | zh_TW |
dc.title | Predicting Firms’ Competitive Actions from Business News Documents | en |
dc.type | Thesis | |
dc.date.schoolyear | 103-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 楊錦生,吳怡瑾,李彥賢 | |
dc.subject.keyword | 競爭行動預測,競爭行動探勘,競爭行動,新聞文件運用,文字探勘,資料探勘, | zh_TW |
dc.subject.keyword | Competitive Actions Prediction,Competitive Actions Mining,Competitive Actions,News Documents Applications,Data Mining,Text Mining, | en |
dc.relation.page | 47 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2015-08-06 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
顯示於系所單位: | 資訊管理學系 |
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