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数据分析与知识发现  2018, Vol. 2 Issue (3): 70-78     https://doi.org/10.11925/infotech.2096-3467.2017.0997
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于情感分类的竞争企业新闻文本主题挖掘*
王树义(), 廖桦涛, 吴查科
天津师范大学管理学院 天津 300387
Mining News on Competitors with Sentiment Classification
Wang Shuyi(), Liao Huatao, Wu Chake
School of Management, Tianjin Normal University, Tianjin 300387, China
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摘要 

目的】在竞争情报分析中, 改进新闻报道信息主题识别效率, 降低情报搜集成本, 提升分析的即时性。【应用背景】适用于企业竞争情报人员通过新闻媒体对企业自身和竞争对手的报道抓取和主题识别, 及时感知重要动态。【方法】使用情感分析API对爬取的新闻报道数据做出分类, 利用LDA识别主题, 并进行可视化分析。采用Python完成数据采集、清洗、分析与可视化等流程。【结果】从共享单车新闻中, 识别出正负面情绪的不同主题, 并且找出对应的主要特征词汇。【结论】基于情感分类的主题挖掘方法有助于企业聚焦自身与竞争对手的主要优势与问题, 可以改进环境扫描与竞争情报的时效性和准确性。

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王树义
廖桦涛
吴查科
关键词 情感分类主题挖掘竞争情报    
Abstract

[Objective] This paper aims to improve the efficiency of topic modeling from news reports, and reduce the cost of competitive intelligence analysis. [Context] The proposed method could help competitive intelligence analysts accomplish environmental scanning tasks with the help of news reports. [Methods] First, we retrieved news stories with the help of a web crawler. Then, we categorized these articles based on a sentiment analysis API. Third, we identified and visualized news topics with the help of Latent Dirichlet Allocation method. We used Python to finish the data collection, cleansing, analyzing and visualizing jobs. [Results] We identified positive and negative sentiments as well as related keywords from news reports on the bike-sharing industry. [Conclusions] The proposed topic mining method based on sentiment analysis helps enterprises identify competitive advantages. It also improves the effectiveness of environmental scanning for competitive intelligence.

Key wordsSentiment Classification    Topic Mining    Competitive Intelligence
收稿日期: 2017-09-29      出版日期: 2018-04-03
ZTFLH:  TP393  
基金资助:*本文系国家社科基金青年项目“基于信息价格动态揭示的社交媒体用户隐私保护研究”(项目编号: 15CTQ017)和天津师范大学杰出青年创新团队项目“数字化时代信息用户与信息行为研究”的研究成果之一
引用本文:   
王树义, 廖桦涛, 吴查科. 基于情感分类的竞争企业新闻文本主题挖掘*[J]. 数据分析与知识发现, 2018, 2(3): 70-78.
Wang Shuyi,Liao Huatao,Wu Chake. Mining News on Competitors with Sentiment Classification. Data Analysis and Knowledge Discovery, 2018, 2(3): 70-78.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.0997      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2018/V2/I3/70
  竞争企业网络新闻情感分类及主题挖掘的技术思路
  基于BosonNLP的情感分类流程
  ofo新闻情感时间序列可视化
  摩拜新闻情感时间序列可视化
  共享单车新闻情感分析数值箱形图
  ofo正面新闻主题建模可视化
  “芝麻”一词在ofo正向情感主题中分布可视化
  调整λ后的主题1关键词排序变化
分类 ofo 摩拜
正面报道
信息主题
智能绿色
押金支付
定位系统
公司合作
红包福利
合作伙伴
车身结构
海外运营
市场份额
负面报道
信息主题
单车停放
交通事故
软件漏洞
单车停放
支付诈骗
软件漏洞
专利侵权
  “ofo”与“摩拜”正负面报道信息的相关主题
[1] Sjöblom S.Competitive Intelligence-Conducting an Analysis of a Business Environment[D]. Tampere University of Technology, 2015. .
[2] Tuan L T.Organizational Social Capital as a Moderator for the Effect of Entrepreneurial Orientation on Competitive Intelligence[J]. Journal of Strategic Marketing, 2015, 25(4): 301-315.
doi: 10.1080/0965254X.2015.1076884
[3] 肖璐, 陈果, 刘继云. 基于情感分析的企业产品级竞争对手识别研究——以用户评论为数据源[J]. 图书情报工作, 2016, 60(1): 83-90, 97.
doi: 10.13266/j.issn.0252-3116.2016.01.012
[3] (Xiao Lu, Chen Guo, Liu Jiyun.Study on Identification of Enterprise Product Level Competitor Based on Sentiment Analysis: Taking User Reviews for Data Resources[J]. Library and Information Service,2016, 60(1): 83-90, 97.)
doi: 10.13266/j.issn.0252-3116.2016.01.012
[4] 王伟, 王洪伟. 面向竞争力的特征比较网络: 情感分析方法[J]. 管理科学学报, 2016, 19(9): 109-126.
[4] (Wang Wei, Wang Hongwei.Comparative Network for Product Competition in Feature-levels Through Sentiment Analysis[J]. Journal of Management Sciences in China, 2016, 19(9): 109-126.)
[5] 唐晓波, 刘广超. 细粒度情感分析研究综述[J]. 图书情报工作, 2017, 61(5): 132-140.
[5] (Tang Xiaobo, Liu Guangchao.Research Review on Fine-grained Sentiment Analysis[J]. Library and Information Service, 2017, 61(5): 132-140.)
[6] 吴应良, 黄媛, 王选飞. 在线中文用户评论研究综述: 基于情感计算的视角[J]. 情报科学, 2017, 35(6): 159-163.
[6] (Wu Yingliang, Huang Yuan, Wang Xuanfei.Research on Online Users’ Reviews in Chinese: Basing on the Perspective of Affective Computing[J]. Information Science, 2017, 35(6): 159-163.)
[7] He W, Zha S, Li L.Social Media Competitive Analysis and Text Mining: A Case Study in the Pizza Industry[J]. International Journal of Information Management, 2013, 33(3): 464-472.
doi: 10.1016/j.ijinfomgt.2013.01.001
[8] He W, Wu H, Yan G, et al.A Novel Social Media Competitive Analytics Framework with Sentiment Benchmarks[J]. Information & Management, 2015, 52(7): 801-812.
doi: 10.1016/j.im.2015.04.006
[9] He W, Shen J, Tian X, et al.Gaining Competitive Intelligence from Social Media Data: Evidence from Two Largest Retail Chains in the World[J]. Industrial Management & Data Systems, 2015, 115(9): 1622-1636.
[10] Fan W, Gordon M D.The Power of Social Media Analytics[J]. Communications of the ACM, 2014, 57(6): 74-81.
doi: 10.1145/2602574
[11] Papadopoulos S, Bontcheva K, Jaho E, et al. Overview of the Special Issue on Trust and Veracity of Information in Social Media[J]. ACM Transactions on Information Systems (TOIS), 2016, 34(3): Article No.14.
doi: 10.1145/2870630
[12] Allcott H, Gentzkow M.Social Media and Fake News in the 2016 Election[J]. Journal of Economic Perspectives, 2017, 31(2): 211-236.
doi: 10.1257/jep.31.2.211
[13] Luca M, Zervas G.Fake It till You Make It: Reputation, Competition, and Yelp Review Fraud[J]. Management Science, 2016, 62(12): 3412-3427.
doi: 10.2139/ssrn.2293164
[14] Filieri R, McLeay F. E-WOM and Accommodation: An Analysis of the Factors That Influence Travelers’ Adoption of Information from Online Reviews[J]. Journal of Travel Research, 2014, 53(1): 44-57.
doi: 10.1177/0047287513481274
[15] Fearn-Banks K.Crisis Communications: A Casebook Approach[M]. Routledge, 2016.
[16] Kleinnijenhuis J, Schultz F, Utz S, et al.The Mediating Role of the News in the BP Oil Spill Crisis 2010: How US News is Influenced by Public Relations and in Turn Influences Public Awareness, Foreign News, and the Share Price[J]. Communication Research, 2015, 42(3): 408-428.
doi: 10.1177/0093650213510940
[17] Du Toit A S. Using Environmental Scanning to Collect Strategic Information: A South African Survey[J]. International Journal of Information Management, 2016, 36(1): 16-24.
doi: 10.1016/j.ijinfomgt.2015.08.005
[18] Cheng X Y, Zhu L L, Zhu Q, et al.The Framework of Network Public Opinion Monitoring and Analyzing System Based on Semantic Content Identification[J]. Journal of Convergence Information Technology, 2010, 5(10): 1-5.
doi: 10.4156/jcit.vol5.issue10.1
[19] Chung W.BizPro: Extracting and Categorizing Business Intelligence Factors from Textual News Articles[J]. International Journal of Information Management, 2014, 34(2): 272-284.
doi: 10.1016/j.ijinfomgt.2014.01.001
[20] Yang C S, Ye H C.Mining Company Competitor/Collaborator Network from Online News for Competitive Intelligence[C]// Proceedings of the 2nd International Conference on Intelligent Technologies and Engineering Systems (ICITES2013). Springer, 2014: 627-634.
[21] Ma Z, Pant G, Sheng O R.Mining Competitor Relationships from Online News: A Network-Based Approach[J]. Electronic Commerce Research and Applications, 2011, 10(4): 418-427.
doi: 10.1016/j.elerap.2010.11.006
[22] Pang B, Lee L, Vaiythyanathan S.Thumbs up?: Sentiment Classification Using Machine Learning Techniques[C]// Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10. Association for Computational Linguistics, 2002: 79-86.
[23] Blei D M, Ng A Y, Jordan M I.Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
[24] 刘启华. 基于LDA 和领域本体的竞争情报采集研究[J]. 情报科学, 2013, 31(4): 51-55.
[24] (Liu Qihua.A Study of Competitive Intelligence Acquisition System Based on LDA and Domain Ontology[J]. Information Science, 2013, 31(4): 51-55.)
[25] Shi Z M, Lee G, Whinston A B.Toward a Better Measure of Business Proximity: Topic Modeling for Industry Intelligence[J]. MIS Quarterly, 2016, 40(4): 1035-1056.
doi: 10.25300/MISQ/2016/40.4.11
[26] Wang B, Liu S, Ding K, et al.Identifying Technological Topics and Institution-Topic Distribution Probability for Patent Competitive Intelligence Analysis: A Case Study in LTE Technology[J]. Scientometrics, 2014, 101(1): 685-704.
doi: 10.1007/s11192-014-1342-3
[27] 潘云仙, 袁方. 基于JST 模型的新闻文本的情感分类研究[J]. 郑州大学学报: 理学版, 2015, 47(1):64-68.
doi: 10.3969/j.issn.1671-6841.2015.01.014
[27] (Pan Yunxian, Yuan Fang.News-text Sentiment Classification Research Based on JST Model[J]. Journal of Zhengzhou University: Natural Science Edition, 2015, 47(1): 64-68.)
doi: 10.3969/j.issn.1671-6841.2015.01.014
[28] Calheiros A C, Moro S, Rita P.Sentiment Classification of Consumer-Generated Online Reviews Using Topic Modeling[J]. Journal of Hospitality Marketing & Management, 2017(13): 1-19.
doi: 10.1080/19368623.2017.1310075
[29] Papanikolaou Y, Foulds J R, Rubin T N, et al.Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA[J]. Journal of Machine Learning Research, 2017, 18: 1-58.
[30] Sievert C, Shirley K E.LDAvis: A Method for Visualizing and Interpreting Topics[C]//Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces. 2014: 63-70.
[31] Underwood T.Topic Modeling Made Just Simple Enough [EB/OL]. [2017-10-25]..
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