基于SVM算法的航天微博情感分析

信息安全研究 ›› 2017, Vol. 3 ›› Issue (12): 1129-1133.

基于SVM算法的航天微博情感分析

徐建忠¹,朱俊²,赵瑞³,张亮¹,何亮²,李娇娇¹

1. 杭州世平信息科技有限公司
2. 西昌卫星发射中心
3. 装甲兵工程学院

收稿日期:2017-12-16 出版日期:2017-12-15 发布日期:2017-12-15
通讯作者: 徐建忠

Sentiment Analysis of Aerospace Microblog Using SVM

Received:2017-12-16 Online:2017-12-15 Published:2017-12-15

摘要/Abstract

摘要： 微博作为人们获取和传播信息的重要平台，是分析掌握大众舆情的重要数据源．为了掌握航天事件对舆论的影响，对航天相关微博数据的进行情感分析，通过人工构建训练数据集、特征设计和SVM模型训练，建立了判断航天相关微博情感正极性和负极性的方法．实验结果表明，该方法有着较高的准确率和召回率，验证了所提方法在航天相关微博情感分析判断方面的有效性．

关键词: 情感分析, 航天, 微博, SVM算法

Abstract: Microblog is an important platform, from which people can obtain and transmit information, and also is important data source to analyze and grasp the public opinion. In order to grasp aerospace event's influence on public opinion, this paper carried out a study on aerospace microblog data sentiment analysis, via manual training data construction, feature designing and support vector machine(SVM) model training, we propose a method to judgment the aerospace microblogging is positive or negative. Experiment result show that, the method discussed in this paper get high precision and recall value, which verify the effectiveness of proposed method in aerospace microblogging sentiment analysis judgment.

Key words: sentiment analysis, aerospace, microblog, SVM

徐建忠朱俊赵瑞张亮何亮李娇娇. 基于SVM算法的航天微博情感分析[J]. 信息安全研究, 2017, 3(12): 1129-1133.

参考文献

[1] Liu B. Sentiment analysis and opinion mining[C]// Synthesis Lectures on Human Language Technologies. 2012:152-153 [2] Wang G, Sun J, Ma J, et al. Sentiment classification: The contribution of ensemble learning[J]. Decision Support Systems, 2013, 57(1):77-93 [3] Wiebe J M, Bruce R F, O'Hara T P. Development and use of a gold-standard data set for subjectivity classifications[J]. Pediatric Clinics of North America, 2002, 13(3):835-842 [4] Pang B, Lee L, Vaithyanathan S. Thumbs up?: Sentiment classification using machine learning techniques[J]. Computer Science, 2002:79-86 [5] Socher R, Perelygin A, Wu J Y, et al. Recursive deep models for semantic compositionality over a sentiment treebank[C] ∥Proc of the 2013 Conf on Empirica1 Methods in Natural Language P rocessing． Seattle，Washington： Association for Computational Linguistics， 2013 ：1631-1642 [6] Zhang X, Zhao J, Lecun Y. Character-level convolutional networks for text classification[J]. 2015 [7] Jansen B J, Zhang M, Sobel K, et al. Twitter power: Tweets as electronic word of mouth[J]. Journal of the Association for Information Science and Technology, 2009, 60(11):2169-2188 [8] Bermingham A, Smeaton A F. Classifying sentiment in microblogs: Is brevity an advantage[C]//Proc of ACM Int Conf on Information and Knowledge Management.New York: ACM, 2010:1833-1836 [9] Zhao J, Liu K, Wang G. Adding redundant features for CRFs-based sentence sentiment classification[C]//Proc of Conf on Empirical Methods in Natural Language Processing, EMNLP 2008. Stroudsburg,PA: ACL, 2008:117-126 [10] Zhou L, Xia Y, Li B, et al. WIA-opinmine system in NTCIR-8 MOAT evaluation[J]. Ntcir,2010 [11] 李寿山, 黄居仁. 基于Stacking组合分类方法的中文情感分类研究[J]. 中文信息学报, 2010, 24(5):56-61 [12] Go A, Bhayani R, Huang L. Twitter sentiment classification using distant supervision[J]. Cs224n Project Report, 2009 [13 ] Barbosa L, Feng J. Robust sentiment detection on Twitter from biased and noisy data[C]//Proc of Int Conf on Computational Linguistics.2010:36-44 [14] Zhao J, Dong L, Wu J, et al. MoodLens: An emoticon-based sentiment analysis system for chinese tweets[C]// Proc of ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining.New York:ACM, 2012:1528-1531 [15] Hu M, Liu B. Mining and summarizing customer reviews[C]//Proc of the 10th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining.New York:ACM,2004:168-177 [16] Miller G A. WordNet: A lexical database for English[J]. Communications of the ACM, 1995, 38(11):39-41 [17] SchutzeH, Hull D, and Pedersen J. A comparison of classifiers and document representations for the routing problem[C]//Proc of the 18th ACM Int Conf on Research and Development in Information Retrieval (SIGIR-95).New York:ACM,1995:229-237.