The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kami mencadangkan kaedah untuk menetapkan polariti kepada maklumat penyebab yang diekstrak daripada artikel kewangan Jepun berkenaan prestasi perniagaan syarikat. Kaedah kami memberikan kekutuban (positif atau negatif) kepada maklumat penyebab selaras dengan prestasi perniagaan, cth "zidousya no uriage ga koutyou: (Jualan kereta adalah bagus)" (Polariti positif diberikan dalam contoh ini). Kami mungkin menggunakan ungkapan kausa yang ditetapkan kekutuban oleh kaedah kami, contohnya, untuk menganalisis kandungan artikel mengenai prestasi perniagaan mengikut keadaan. Pertama, kaedah kami mengklasifikasikan artikel berkenaan prestasi perniagaan menjadi artikel positif dan artikel negatif Menggunakannya, kaedah kami memberikan kekutuban (positif atau negatif) kepada maklumat penyebab yang diekstrak daripada set artikel berkenaan prestasi perniagaan Walaupun kaedah kami memerlukan set data latihan untuk mengklasifikasikan artikel berkenaan prestasi perniagaan kepada positif dan negatif satu lagi, kaedah kami tidak memerlukan set data latihan untuk menetapkan kekutuban kepada maklumat sebab Oleh itu, walaupun maklumat kausa tidak muncul dalam set data latihan untuk mengklasifikasikan artikel berkenaan prestasi perniagaan kepada positif dan negatif wujud, kaedah kami dapat menetapkannya kekutuban. dengan menggunakan maklumat statistik set artikel terperingkat ini Kami menilai kaedah kami dan mengesahkan bahawa ia masing-masing mencapai ketepatan 74.4% dan 50.4% ingatan semula untuk memberikan kekutuban positif, dan 76.8% kejituan dan 61.5% ingatan semula kekutuban negatif.
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Salinan
Hiroyuki SAKAI, Shigeru MASUYAMA, "Assigning Polarity to Causal Information in Financial Articles on Business Performance of Companies" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 12, pp. 2341-2350, December 2009, doi: 10.1587/transinf.E92.D.2341.
Abstract: We propose a method of assigning polarity to causal information extracted from Japanese financial articles concerning business performance of companies. Our method assigns polarity (positive or negative) to causal information in accordance with business performance, e.g. "zidousya no uriage ga koutyou: (Sales of cars are good)" (The polarity positive is assigned in this example). We may use causal expressions assigned polarity by our method, e.g., to analyze content of articles concerning business performance circumstantially. First, our method classifies articles concerning business performance into positive articles and negative articles. Using them, our method assigns polarity (positive or negative) to causal information extracted from the set of articles concerning business performance. Although our method needs training dataset for classifying articles concerning business performance into positive and negative ones, our method does not need a training dataset for assigning polarity to causal information. Hence, even if causal information not appearing in the training dataset for classifying articles concerning business performance into positive and negative ones exist, our method is able to assign it polarity by using statistical information of this classified sets of articles. We evaluated our method and confirmed that it attained 74.4% precision and 50.4% recall of assigning polarity positive, and 76.8% precision and 61.5% recall of assigning polarity negative, respectively.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.2341/_p
Salinan
@ARTICLE{e92-d_12_2341,
author={Hiroyuki SAKAI, Shigeru MASUYAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Assigning Polarity to Causal Information in Financial Articles on Business Performance of Companies},
year={2009},
volume={E92-D},
number={12},
pages={2341-2350},
abstract={We propose a method of assigning polarity to causal information extracted from Japanese financial articles concerning business performance of companies. Our method assigns polarity (positive or negative) to causal information in accordance with business performance, e.g. "zidousya no uriage ga koutyou: (Sales of cars are good)" (The polarity positive is assigned in this example). We may use causal expressions assigned polarity by our method, e.g., to analyze content of articles concerning business performance circumstantially. First, our method classifies articles concerning business performance into positive articles and negative articles. Using them, our method assigns polarity (positive or negative) to causal information extracted from the set of articles concerning business performance. Although our method needs training dataset for classifying articles concerning business performance into positive and negative ones, our method does not need a training dataset for assigning polarity to causal information. Hence, even if causal information not appearing in the training dataset for classifying articles concerning business performance into positive and negative ones exist, our method is able to assign it polarity by using statistical information of this classified sets of articles. We evaluated our method and confirmed that it attained 74.4% precision and 50.4% recall of assigning polarity positive, and 76.8% precision and 61.5% recall of assigning polarity negative, respectively.},
keywords={},
doi={10.1587/transinf.E92.D.2341},
ISSN={1745-1361},
month={December},}
Salinan
TY - JOUR
TI - Assigning Polarity to Causal Information in Financial Articles on Business Performance of Companies
T2 - IEICE TRANSACTIONS on Information
SP - 2341
EP - 2350
AU - Hiroyuki SAKAI
AU - Shigeru MASUYAMA
PY - 2009
DO - 10.1587/transinf.E92.D.2341
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2009
AB - We propose a method of assigning polarity to causal information extracted from Japanese financial articles concerning business performance of companies. Our method assigns polarity (positive or negative) to causal information in accordance with business performance, e.g. "zidousya no uriage ga koutyou: (Sales of cars are good)" (The polarity positive is assigned in this example). We may use causal expressions assigned polarity by our method, e.g., to analyze content of articles concerning business performance circumstantially. First, our method classifies articles concerning business performance into positive articles and negative articles. Using them, our method assigns polarity (positive or negative) to causal information extracted from the set of articles concerning business performance. Although our method needs training dataset for classifying articles concerning business performance into positive and negative ones, our method does not need a training dataset for assigning polarity to causal information. Hence, even if causal information not appearing in the training dataset for classifying articles concerning business performance into positive and negative ones exist, our method is able to assign it polarity by using statistical information of this classified sets of articles. We evaluated our method and confirmed that it attained 74.4% precision and 50.4% recall of assigning polarity positive, and 76.8% precision and 61.5% recall of assigning polarity negative, respectively.
ER -