基于SVM结合依存句法的金融领域舆情分析
Sentiment analysis in financial domain based on SVM with dependencysyntax.
查看参考文献16篇
文摘
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用户的情感倾向与市场波动之间的联系,对金融市场的监控和股价异常处理有着重要作用,因此针对金融领域用户生成的文本进行情感分析很有意义。然而,由于金融领域文本的术语比较多,句子比较长,以及缺乏现成的情感语料库,所以针对该领域的情感分析研究目前还比较少。根据金融领域文本的特点,充分考虑到金融领域情感词的特征、单个句子中词语的位置权重以及情感词相互间的修饰关系,提出SVM分类结合Stanford句法依存分析方法,计算文档的情感值。利用重要财经网站上抽取的金融领域数据进行实验,综合值F达到了82.1%,比文献中其他方法更为精准。 |
其他语种文摘
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The linkages between users emotional tendencies and market fluctuations to monitor and handle the market price of exception play an important role, so the sentiment analysis of user-generated text in financial sector becomes meaningful. However, due to the longer sentences and term of the financial sector, and not many ready-made emotional corpus, sentiment analysis research in this field is still relatively small. Based on the characteristics of financial sector, it uses the SVM classification with Stanford syntactic dependency analysis to calculate the document emotional value which fully takes into account the characteristics of emotional words, the words position weights and the modification of relationship between each other. Through the experimentation online extraction data which from the important financial website, the integrated value of F reaches 82.1%, more accurate than other methods in the literature. |
来源
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计算机工程与应用
,2015,51(23):230-235 【扩展库】
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DOI
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10.3778/j.issn.1002-8331.1311-0180
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关键词
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金融领域
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情感分析
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位置关系
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支持向量机(SVM)
;
依存分析
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地址
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1.
华东理工大学信息学院, 上海, 200237
2.
上海证券交易所技术部, 上海, 200120
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语种
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中文 |
文献类型
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研究性论文 |
ISSN
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1002-8331 |
学科
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自动化技术、计算机技术 |
基金
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国家科技支撑计划项目
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文献收藏号
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CSCD:5569163
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参考文献 共
16
共1页
|
1.
Gaskell P. An investigation into correlations between financial sentiment and prices in financial markets.
Proceedings of the 5th Annual ACM Web Science Conference,2013:99-108
|
被引
1
次
|
|
|
|
2.
姚天昉. 文本意见挖掘综述.
中文信息学报,2008,22(3):71-80
|
被引
36
次
|
|
|
|
3.
Pang B. Thumbs up? sentiment classification using machine learning techniques.
Proceedings of EMNLP-02,the Conference on Empirical Methods in Natural Language Processing,2002:79-86
|
被引
1
次
|
|
|
|
4.
Kim S M. Automatic identification of pro and con reasons in online reviews.
Proceedings of the COLING/ACL-2006,2006:483-490
|
被引
1
次
|
|
|
|
5.
Goldberg A B. Seeing stars when there aren't many stars:graph-based semi-supervised learning for sentiment categorization.
Proceedings of HLT-NAACL 2006 Workshop on Textgraphs:Graph-based Algorithms for Natural Language Processing,2006
|
被引
1
次
|
|
|
|
6.
Zhang Changli. Sentiment classification for Chinese reviews using machine learning methods based on string kernel.
Proceedings of the 3rd International Conference on Convergence and Hybrid Information Technology,2008
|
被引
1
次
|
|
|
|
7.
Tan S. An empirical study of sentiment analysis for Chinese documents.
Expert Systems with Applications,2008,34(4):2622-2629
|
被引
15
次
|
|
|
|
8.
Turney P D. Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews.
Proceedings of ACL202,40th Annual Meeting of the Association for Computational Linguistics,2002:417-424
|
被引
1
次
|
|
|
|
9.
Hu M Q. Mining and summarizing customer reviews.
Proceedings of DD-2004,2004:168-177
|
被引
1
次
|
|
|
|
10.
Kamps J. Words with attitude.
Proceedings of the 1st International Conference on Global WordNet,2002
|
被引
1
次
|
|
|
|
11.
朱嫣岚. 基于HowNet的词汇语义倾向计算.
中文信息学报,2006,20(1):14-20
|
被引
145
次
|
|
|
|
12.
徐琳宏. 基于语义理解的文本倾向性识别机制.
中文信息学报,2007,21(1):96-100
|
被引
55
次
|
|
|
|
13.
Ahmad K. Sentiment polarity identification in financial news:a cohension-based approach.
ACL,2007
|
被引
1
次
|
|
|
|
14.
Tetlock P C. Giving content to investor sentiment:the role of media in the stock market.
Journal of Finance,2007
|
被引
1
次
|
|
|
|
15.
李国林. 基于语素的金融证券域文本情感探测.
计算机研究与发展,2011
|
被引
1
次
|
|
|
|
16.
徐睿峰. 基于多知识源融合和多分类器表决的中文观点分析.
第三届中文倾向性分析评测会议(COAE),2011:84-94
|
被引
1
次
|
|
|
|
|